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   "source": [
    "# Protein Folding Algorithm\n",
    "\n",
    "Protein folding is the descriptions of a three-dimensional structure of amino-acids chain, arranged in space to become the biologically functional protein.\n",
    "Understanding the structure of protein is highly valuable for various application within medicine.\n",
    "While so, discovering the structure of proteins is an highly intricate problem, as each protein is constructed of a chain of hundreds to thousands amino acids, and the number of configurations is roughly evaluated to be $3^{2(N-1)}$ with N the number of amino acid [1]. The exponential growth of conformations with the chain length N makes the problem very complex for classical computers. For a quantum computer, we algorithm which grows linearly with the number of amino acids N were conceived, as the one simulated hereby.\n",
    "\n",
    "In this demo, we present a method of achieving a folding geometry for a given amino acid sequence. Following paper [2], we create a Pyomo optimization model, and send it to Classiq's Quantum approximated optimization algorithm (QAOA) which finds the configuration with the minimal energy. The results are later visualized and compared to a classical solution."
   ]
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   "source": [
    "## 0. Pre-requirments\n",
    "\n",
    "The model is using several Classiq's libraries in addition to basic python tools."
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import pyomo.core as pyo\n",
    "from sympy import *"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 1. Define the Optimization Problem\n",
    "\n",
    "Follow the paper [2] we created a python pyomo model to describe an optimization problem where a pramatrized cost expression characterise the geometrical configuration of a protein (amino acid sequence).\n",
    "\n",
    "Without too many details, the paper places the protein on a grid of tetrahedral lattice. Each amino acid can be located in any vertex of the lattice, and an index is set to each amino acid. Since each vertex has four neighbours, after locating an index in space, the next location will be set by pointing one of the four directions indicated by an integer $[0,1,2,3]$. In order to do ascribe a direction, we assign each amino acid 2 qubits, thus mapping each index to a direction:\n",
    "$[00] \\rightarrow 0$ ;\n",
    "$[01] \\rightarrow 1$ ;\n",
    "$[10] \\rightarrow 2$ ;\n",
    "$[11] \\rightarrow 3$ .\n",
    "For convenance of the coding, each even and odd index of amino acid, has an opposite meaning in space. I.e., \"0\" will mean \"left\" for odd index, but \"right\" for even index.\n",
    "\n",
    "For Protein of $N$ amino acids, we have $N-1$ directions (or edges). However, since the first two direction only set the orientation of the molecule in space, but do not determine the relative location of the amino acids, we are left with $N-3$ directions (since in thethrader lattice, there is an equal angle between each three vertices, the relations will be the same for the first three regardless of the chosen direction). Thus, the needed number of qubits to describe the directions in the lattice  is $2(N-3)$, and the two first directions are set arbitrarily.\n",
    "\n",
    "Next, we need to set the Hamiltonian, i.e. the cost function that will be sent to minimization. The Hamiltonian consists of two terms: $H_{gc}$ describing geometrical constraints, and $H_{int}$ describing the interactions between the amino acids (the paper also discusses a third, chirality constraint which is only relevant to side chains, which were not considered in the scope of this algorithm):\n",
    "\n",
    "* $H_{gc}$ - prevents two consecutive direction to fold back. Since we used a convention where odd and even index has an opposite meaning in space, it is sufficient to add large penalty term if two consecutive direction are the same.\n",
    "\n",
    "* $H_{int}$ - For the interaction, we define extra qubits, which determine if the interaction is \"turned on\". If so, they add negative (beneficial) energy term $epsilon$, which indicates the interaction between amino acids. $epsilon$ is a nearest neighbor (NN) interaction tern, relevant for distance 1 only, therefore we add a penalty for other distances. Note that currently, we are taking the interaction to be constant value ($epsilon$) regurdless of the type of amino acids interaction. This can be easly modified by insering the Miyazawa & Jernigan table [3].\n",
    "\n",
    "Since it is trivial that following amino acid in the sequence will be nearest neighbors its irrelevant to calculate the contributing energy from such interaction (it will add a constant number regardless of the qubit\u2019s value). In fact, due to the structure of the tetrahedral lattice, only amino acids further than 5 in the sequence might have non-trivial interaction. Thus, the number of possible interactions (and thus the number of interaction qubits) is (N-5)*(N-4)/2 (calculated via arithmetic progression).\n",
    "\n",
    "In addition, we prevent folding back into the chain, while encouraging distance 1 interactions, by making sure that the indices following the two interacting amino acids, do not overlaps with the interacting amino acid themselves. In other words,  for NN $(i,j)$,  {i-1,i+1} must be at distance 2 (else, they will be at distance 0, i.e. overlapping). To account for that, a penalty is added to the interaction term."
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   "source": [
    "def folding_hamiltonian(main_chain: str) -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel(\"protein_folding\")\n",
    "    N = len(main_chain)  # number of amino acids\n",
    "\n",
    "    # Calc number of possible interactions:\n",
    "    Ninteraction = 0\n",
    "    if N > 5:\n",
    "        Ninteraction = int((N - 5) * (N - 4) / 2)\n",
    "\n",
    "    # Define the variables:\n",
    "    model.f = pyo.Var(range(2 * (N - 3)), domain=pyo.Binary)\n",
    "    model.interaction = pyo.Var(range(Ninteraction), domain=pyo.Binary)\n",
    "    f_array = np.array(list(model.f.values()))\n",
    "    interaction_array = np.array(list(model.interaction.values()))\n",
    "\n",
    "    # Setting the two locations:\n",
    "    a = np.array([1, 0, 0, 1])\n",
    "    full_f_array = np.append(a, f_array)\n",
    "\n",
    "    # Define Hgc:\n",
    "    T = lambda i, j: (1 - (full_f_array[2 * i] - full_f_array[2 * j]) ** 2) * (\n",
    "        1 - (full_f_array[2 * i + 1] - full_f_array[2 * j + 1]) ** 2\n",
    "    )\n",
    "    L = 500\n",
    "    model.Hgc = 0\n",
    "    for i in range(N - 2):\n",
    "        model.Hgc = model.Hgc + L * T(\n",
    "            i, i + 1\n",
    "        )  # adds panelty if two consecutive index has the opposite direction\n",
    "\n",
    "    # convert {0,1}^2 to 4 functions, each giving 1 for one vector and 0 for the others:\n",
    "    fun0 = lambda i, j: (1 - full_f_array[i]) * (1 - full_f_array[j])\n",
    "    fun1 = lambda i, j: full_f_array[i] * (1 - full_f_array[j])\n",
    "    fun2 = lambda i, j: full_f_array[j] * (1 - full_f_array[i])\n",
    "    fun3 = lambda i, j: full_f_array[i] * full_f_array[j]\n",
    "\n",
    "    # calculate distance between i,j amino acids:\n",
    "    d_units_0 = lambda i, j: sum(\n",
    "        [((-1) ** k) * fun0(2 * k, 2 * k + 1) for k in range(i, j, 1)]\n",
    "    )\n",
    "    d_units_1 = lambda i, j: sum(\n",
    "        [((-1) ** k) * fun1(2 * k, 2 * k + 1) for k in range(i, j, 1)]\n",
    "    )\n",
    "    d_units_2 = lambda i, j: sum(\n",
    "        [((-1) ** k) * fun2(2 * k, 2 * k + 1) for k in range(i, j, 1)]\n",
    "    )\n",
    "    d_units_3 = lambda i, j: sum(\n",
    "        [((-1) ** k) * fun3(2 * k, 2 * k + 1) for k in range(i, j, 1)]\n",
    "    )\n",
    "    d = lambda i, j: (\n",
    "        (d_units_0(i, j)) ** 2\n",
    "        + (d_units_1(i, j)) ** 2\n",
    "        + (d_units_2(i, j)) ** 2\n",
    "        + (d_units_3(i, j)) ** 2\n",
    "    )\n",
    "\n",
    "    # define Hint:\n",
    "    epsilon = -5000\n",
    "    L2 = 300\n",
    "    L1 = 500\n",
    "    h = lambda i, j: interaction_array[\n",
    "        sum([N - 5 - k for k in range(0, i + 1, 1)]) - (N - j)\n",
    "    ] * (\n",
    "        epsilon\n",
    "        + L1 * (d(i, j) - 1) ** 2\n",
    "        + L2\n",
    "        * (\n",
    "            (2 - d(j - 1, i)) ** 2\n",
    "            + (2 - d(j + 1, i)) ** 2\n",
    "            + (2 - d(i - 1, j)) ** 2\n",
    "            + (2 - d(i + 1, j)) ** 2\n",
    "        )\n",
    "    )\n",
    "    model.Hint = 0\n",
    "    for i in range(N - 5):\n",
    "        j = i + 5\n",
    "        while j < N:\n",
    "            model.Hint = model.Hint + h(i, j)\n",
    "            j = j + 1\n",
    "\n",
    "    # setting the objective:\n",
    "    model.cost = pyo.Objective(expr=model.Hint + model.Hgc, sense=pyo.minimize)\n",
    "\n",
    "    return model"
   ]
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   "source": [
    "## 2. Create your Protein Sequance\n",
    "The user defines the amino acid sequance as a string, which is sent to the `folding_hamiltonian()` function in order to create an optimization model for the sequance."
   ]
  },
  {
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   "execution_count": 3,
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   "source": [
    "my_protein = \"ABCDEF\"  # ABCDEFG\"\n",
    "protein_model = folding_hamiltonian(my_protein)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 3. Optimize Using to Quantum Optimization Algorithm\n",
    "\n",
    "We will now create a QAOA model for the optimization problem. The results of the model is the sequance of qubit values giving the minimized energy for the protein. In order to optimize the results, we recommend the user to explore the number of repatitions for the model (`num_layers`) and the number of iterations for the optimizer (`max_iteration`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab51bfe3-6678-4e5a-bbeb-a1f77d13e439",
   "metadata": {
    "execution": {
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   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=5)\n",
    "\n",
    "optimizer_config = OptimizerConfig(\n",
    "    max_iteration=100,\n",
    "    alpha_cvar=0.7,\n",
    ")\n",
    "\n",
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=protein_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
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   "id": "0793f1a5-4efd-4fb8-8c2d-4518b6fac00d",
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   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39bbd2f6-3aeb-4844-8143-ef639fdb5f22",
   "metadata": {
    "execution": {
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   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f27ef260-7d45-49b6-9a1b-8b321125af2a",
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    "execution": {
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   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"protein_folding\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b05f8f5-705d-4aa4-8737-5cc38ffa843e",
   "metadata": {},
   "source": [
    "Now we can create a quantum circuit using the `synthesize` command and show it"
   ]
  },
  {
   "cell_type": "code",
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    "execution": {
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    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/4e4d55be-3472-4fed-bfc8-a2c76866dffc?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7e7f4a-388d-4ef1-90e5-029db6909f0c",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b9935cf-ed32-40db-9866-7e8a43945950",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349dfe2e-e469-4f6e-a421-12fe1ba61e1a",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
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    {
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p+pCbconmAKR59N3vfheYx0KARZpGoR9DU8KoH/zgB1STyOSmSexqHFidHPCnQqxO4owq48BCOQDagUDIdxKiJHGLERjrMfJaQ3h63333EYi8RqFExyWaQqCKeK4ABlABNvz5n/85qQAGwAAPcMUVvOFKcvJkBJf4qmSjSYP6wA/ZgnwEkgOzioAZ4uOICc69//3vx3jLLSogGYL6+AOooCzAFXoAFYCcDgSZ4IhDtkQjZ+gnnJw1IeHgOpGZewX8oJhSR+pFceAQ8YkAqikZZAU2U33GcbFakwOqPJZ2FHGKxlAP0OLorIC1sBEGQgxpuUXxJTl+PKAg4VzVtg9GkhUOZlI6A72QAbVc6angKJTI1BeSCCdnAnXeFiHgKGmpOJzHMgEAg+vaiKj7z372s3kKt6kRHNZ53YQwYA/uUkFygCGgshIMH+hwBBniMWccWJ0cMABene1iVC2SAyq1VcoraqJyIbtBAoQyQh+EAH7IXTEP1NGSgBYFaeQ4gaAsCf/oj/6ISdQYZpHv5KkQ8ohHPALkIDIZEhMPKEW5inDkDzYzb0vzJ3Py4Ur+4MfrXvc6BoyZssTAMACM2od6ytOADCXm6K/UFMJwmhXkab9B+xCo3R/+8IeBOsASg/BLX/pSCFDIhHLGfdHjqQigrgDMUmZGXsmKmdJEY+GTdlnAe3oDhFM7rvdTCyoLdoLTzH2DmTAHHCW5dg7Ik4JAXzKBIRSNw08SmBZkS3W0IFIRnxzQxRlaRkenwwS1dHroPaBPa3NrhuRjzjiwOjlgALw628WoWiQHENAId7VeKhhrRgpFiH69VSjFj4wGhNC9kPiqWoGjCHeggjjgDcADDPCUcJAAfYunIAc4AdCCymRCQXh0sjQDkCA0gYoBzOAFFaAHqsAtHAZYwI9bdFxVhZUkrhCPgySstWiBGKjpDShc8ZSiQSOwhw4E4RhpmbtEfMLRF0FcqFWdEmpBI6VNr9BMiWTClacgLpPOGCSmc4D9nFFeHlFfNGmeUl9qzSPypNbkz1NYxFXZSDg1IhpdEBxgjPbPUxzMhCTM11QBD0+hBMfQOHyAY6jCRIMe6gJtVAcPGjMFkSfEaC20LQgBTZkuBzNJRW7aWJAEecRECda+AtG0prBX6HCOuhC/eWf/jQOrjgM2BrzqmsQIOhoOKMwg1skEKY/gxq8oBWAQQjgwgPgGL/EDXTwF81TiEwLGALRMznr+85/PEhfGQcEGMAZRrlCngLRnzx4iYwdG7iPo0SwxPlPExRdfrKWDFkxRJoSnxARLuOKYwMyw6+c+9znSPuUpT1HY0EdQQtEkp7jXv/71UMukX64EgqzEwUNB6M0QzIxiQrS+zD0mH/RaegYUR4eA0lHciQzZ1Ejrq5QQyERoIJZJTAzEMm7KxGMYBZ6R24tf/GK01dtvv53MAU4qC1UwgUyoAh6KVkqAYTwkx4LNQC+GX25BccbCwUUikwRwffnLXw54f+hDH+KpJqEuPKWOXFU1JxqPqB00EI0itL7UgtFfAJgRa/zEhxiag0rhgVSaj1qQnFTat8Cj2I/H0BcmmFvNHDANeDW3jtG2YA6AMQoVpASNuF599dWYSZHd3KI4IpSf+MQnAi2YVZH4qKSKYUh8/Eh5BjtVcLOo9Prrr3/AAx7AfGN0TeJfeeWVIBCTp8iNOMQHqNDe8FMusME6GeYtM6zLDGrwj+W2aKsAHgUBkCAreMYjSmECEegL/AAeRCBDrlALYAMwwCGaHMuQyBBNF4KpFEmYdP1Xf/VXIDeGcaZcYVVmV68bbrgBOGdAF0usEgZmg2eQR4YQBnlkCAF4goKYm81oNGtnQV/gmZhAGqne+973Mu+Jp1jLUSsxU1NHpijTayEO6jhoSibcoi4Tnxp9+tOfxqIObdBAQdAMJSAxfmoK8YT/4z/+I8yEM3AeBpIh88tgLKSC38SENvLnFjphFwXBCgL/3//7f0TGKs5EM21QBq3JjXF9ujJMriYTpsuhYdOOsIIuBUZ1VpfBWzI0ZxxY1RzgqzNnHFhLHECCUx3EN2tnVaNC6PMRAhUq5ZmHTAS0NAQ6cUDKoPpEA5aCWzCDidDYTglH1QMGmLvEU7S9L3/5y2TOnGGNrJoufky7rKshCZt78BQkQ4cjHKBVXZyYqG5kyGioBmq46m3EBIQ0T5Tsf/qnfyIfoAUkYxkxRmnAj6f0BtB6MVNTIzCeNcdgIVjLIwCSVbaQSgi3cAMsRGnWtUOEQABX9FTSglIMSCsBQbm33HLLX//1X+tIOdfHPe5x//qv/0oSIqCJYjkA77kFNbniqAVqKDUFOEE+FGgWKNNx0YKUKvhMPsAqjjVRb33rW+kVaVpqwUxmMnGZifmaadhMvKK3pCHM82JxFKnoo1A607/pBukj+gGswGahMBWhGwEMo2ozM0ufBnnqrV2NA6uNA7KpOrLAnHFgbXAALMEhqVEZtUa84QAY8AYAA2OgCMKaONwCIUTDA0rhR4IDWviJQFpFa/1igRZCAD+SkwTHLRCiKIWgJ0TxRtMylqmwDWAwwoqOSHwioN2S4e/93u9R0Pe//32USHIOiIE8onFLHC2C21YHbUqA0qaaKMZzVXOJSQgZBqUH4Vp0kBX5qMKtzNFUxIFRmnMQszUhLIJChq6BTEAddmkI1SchZWED0LowBxsbPpZ2HpEVVx5hLuaK0+IIJ3OtprI3KJQI9IpgHQmhk6dKBlduIR42EodCcVoF0hJO6TzSfLjl6ZycgyLMYxxYDRywMeDV0ApGw5JxADQFAhHrKIIq/ZHC4AQjjgQCEgqQeJDmwAnx8SPWgRN0L3RlLLeAkOIQUp5oiHilj1vwAymvt6Cv+kmOuobopyxghqeKvmjJhKOMEg0HJPCIaVnM1GXqEAo08QnR/KEEP9G4AhsE4gducZqQ0gmHQmjTcJKrVVYhioTUFEfpRNb41E7HuXlKKrUkkzPRtDi6JopS0E/OPKLKXElI5ADPlJkkAX0Jh134lTAYS87aeyAtej+cZIE1EXiEg2n4aQL1UBwJFTjx6y2MoskIp2hCQF+S4KeOFK1kwCIccXDE0Ud0BYgJtUSmIfDj1E8cvbWrcWB1csA04NXZLkbVIjkAEiCFEfqaHkmtQplwArkCYCr6iaAeEAhs4BaprYoX0IVfoZo4qodpPmCPjq0Sh9yIw1PiaGS2gGDelm7uwUjkf//3f7MlBTZe8idDhmzZQ+Ob3/wmui9mXoy0BIIoelW8DGgLyNOKcKUuQI6CCjhHddQPSQAb+QQxyRDaFCODQNVZg1s8gBZX7WqQoYJcwDGNCVXgIo+UpYyUA/kkxCzM2C21piCW6mKyftSjHoXpG7s6RngeMZCMHkwmmOt1IICqkZArnORKhlSHCJjTgdtW+pUJyl4iwGHIIAIe5TOBEMw16B/gb3XUggikmsOE1jjmNw6sPAd4180ZB9YSB5C8OvxJpfDj5tQOKEL/C+LwlFvEPR6giyuQNicJt+QDPCvctqbVsVstBV2TlamsUAInsAxzbIBqnyABkMD4MR88ZmemDhFCnlooj+YXxyOQD3qISaFKWGs0wIwIhIDrGg6SKRmKc9QCpwRrBC2OAdQ55WKy1giUQnwywWmhSqc+JVtu51DCLQSw6xb9CWoH4DGCq2O00KMJD3mFBnID0fUpftpFCdMQGEsFYbXSoBWBn3MIIDLaM1doJj5Xja+Z2NU4sJo5YBrwyveBjIIl5wCfnGpXrTmDcwASk4AQ5ahfyHE0SOS12k5BFwKR3Xg0hAg8JQfUrFb9jBDi8FR1R/S5QJND86MUQhQtyA0kBrYBY0giFfmo/qdJCCETpVavUAgZBBKtlfjAD8ZAdqAIgk9EJkkQIfBo5tyCr9Ra1eXgKbSRlqpREA6CCWlVKElOkjkVJ7kqwVSfR0BvYCvmNiiRaKAv3ABQ4aHayQkkAtVsLUXpoWjQV00LhJAqqCCPlHI4g/UCUjWC8o0MtbE0nyA3Us2nvDWO+Y0Dq4EDBsCroRWMhiXjAKCCmNbswMLA0yr3Fep4pIJeoyHZAwgECYI4mgOCHojCD/AEcEIcQoBSRaMgCVCB9NfxTiKgtKH1alq0OrAQf+A0LbeK3BpOVtxyxQE/OPAJ3AJ1WjsWKO4KP4AcMQEtgIcMyURA1WFVEIcciOCybJBPAJakDRCRQjV/omlysiKC4hyPCKQHQNX0qtRqDkq/QjI0kEPQLWgFVE0CncSHpWRFBwWGU0cCcbBO664IqmwnXOtOtuRA6drKVErHFKAhQGLiKBPIM6iFlmtX48Cq4oAB8KpqDiNmaTigYEBeCG6ksGaqYh2JLGLerbvlEfiEZCdQgVCvqrMSH8cjXCtZBIIoyH3FMI0TwFgAeIAEEEJBwB6BoEIAyUGhZEsmABXYBtLggQCSkFDJxs8jXCsBBOIUnwinaCKTliICZCJcx7YJ1OQBCgJaOjKq9CtckcOcapIDpUAP/NHSyRw/5SqpetX6wgTiaA70P8ifDOkMER7otZoJVwiGWq1gEIiHDMm8tbLaHK1VIBU0BNyeE4HilDOtmbQWYX7jwKriQNsBWL8Qvgq+9qCHHnzSgbQKvjE8fEUqXFo/M3IQQeimm6JD6JemAksjq7iBufoZk49+ihpCzMCjAgjCKF1n3xBzvjhYVe1kxBgHjAPGAePA/XMAkY7kD3q69x95NTxtOwCDlKCmVhUMdhjqj+KAxxpO3xwjGJ1WusygKX5CuMXTalEkshq4wFoAleSaufbENasAs7kNYDWggS488zWIT68cQyLEEIfcQF/iaA52NQ4YB4wDxoFVxQFgVRUtBDV+1ZfwI8/ZzQ2dCtnOnAOlOZD8q6oKhySm7QAM2uHAS1xAgeqg4DEhqg1rHPovisqgr9rHYKsanZTvRMY8CHYSgSQgKB5coEmTIc3DLZ0gkij2q4dWYf4Ic0bYyY/NBIgW0ENkJSYIMY9xwDhgHDAOrBIOIL1V2mO5hCRAAQGOYGfJO0d9s2eq0tkKBKuE8vsnY2bt4P3HW/RTui2t0ItGS1Y6BAWawkQgFvyDcfCUaRTaeWHeCoFE4KpFE675oLyCuKoKK2wTQedfkBX+IBVJuNVmw8MtjUfRbC3LZoEkwVEu2QLq2qUimjnjgHHAOGAcWFUcUFyAJJXqiGs8yP9t27bpPHylVjGi1Q66qmpxCGKAwGVwweo99pJlD3rwD1J0W11KpyPDoZ4BcSAomwcRTirsw8zpOPfccwFLpr2wsJJNdtTgQCp2dWcreRJihWAL+KAiNAx+rsQJAmkw1mMQWZ8CvbQTii8RAOYgmnmMA8YB44BxYPVzAAGu84EQ6cGCcshmBHP1E68Utl0D1lWDGAp00hOwx+kof/iHf/iKV7xCtVvoABRhJRu4f/KTn0S7BRRVH9VUxAeSf/GLX3Dm9lve8hbU3M9+9rOKo2A5Z7BwOsqvf/3r888/nzHjP/mTP2FUQFd6BLovkXHkrKMItBa3FEG2WjqPXBS7GAeMA8YB48Cq4wCCWqV0oAqjXCHJkfZYQ1nmp5PtCSEaQLDqKnAYgtoOwMGiScAPB2RychkDvRx2BpSqrUA5y1PswzhFUPa6Y/sCzmLjDDjQFys/jmbglFbOiUOHBoaBc058o0kYfr/gggv+/d//nS1256yzpOKUooPBxKRozBTko8YKiqbN1H8YFlmwccA4YBwwDqwkBxQjoCDwKDXgLsZR/Ch4IAiSvLNm8yzHZuVYjJVZ2JPpmwCBQC8OBZRbPDwFINkml1NXOSr83e9+N5OkOE2FmGyuu337duzMqrZyshvsZi97Hv3yl78EzmE3fCcQYP7Nb35DOKhMhkAsXSQQl4RYJAjkChLrlSREUKqW9kqm+nPZ1r0G49Du50preeTiLW3ZlptxwDhgHFg3HECGM0CpCIJIB4Opul47hQdtB2DwD6UWdsApXXTLEd86wQos5KkaolGLv/jFL6LscgAqSPyCF7wAsISVTNrSo13o2hCiM7DuuusuWM9hcDwCcfHjUHyBc1BZO0TkTJNQKHDOtGcSoosD6lACKuPUyo1nCdVf8JXFTPxk9heXes1rYO5uBjQUipsQPQuNJYE544BxwDhgHDgkB1CoCEcDRmjjQWdTDxCgjxD4hATzdg+ZyWoLbDsAA2+gI3AIv7hSf45GIQSWMXuNp8AwWiym4xe96EXAJCeHX3rppbfffjtQSteGVBxLTjTVnkkO0MJimgF4Vls/0eA7uAtCMylOkVsZjREbezUaM0XgwP52N4AqvM1SnAaMEqxY22hRjgnBGQY3OWX/jQPGAePAeuNA2wEYhmr3hDnMqmvSTwE+Cdy3bx9P6b9gi8ZDILoyMHnSSScBt5xxBh4D0trNUe0ZhZiDVoBwoJRzSVGFsW8DveA0Vmty5iAaABiP5km2oDWKMiHAP0UQ0lYHQ/Xnq8GtBbb620qEZW4cMA4YB4wDq54DywHAgJ9CL8oueAkQKhaCi/BHLfgsLsIPjhKT1UfAJ1DKDOrHP/7xLNL91a9+xVMOFr3qqqsAVIaKecr5o7fddpvat4Fzzl5lMRKGZWCbyIRQHB71g+Ig/ebNmzWkrVdwVqAW7Vb+hb2GY3LIazgAdhdXvijBzlbt7uxiHDAOGAeMA+uKA20HYJAPpRaeArRAIO6aa67Bwgwqo7My6MsVPGZK8w9/+EP02p///OfMdj7hhBNYlUQctGGs069+9auZYMUBZy9/+cu5Pf7448kQPxPQX/KSl3C2OeiL41Rw8JtooK9iubYlfhylMLO63a0LvjqegqzOysy92JnDoK+MYMxyzlwdMgyexRS7MQ4YB4wD64UDMn+pnY7pxwCwlgAYX3zxxSivyly0VXDxzW9+M7ZoDvRGPcVEzJzn1772tZigdWE19mRQ+ZWvfCVJmEv1+te/Hq1Xc+MRQM46YIzbrAN7//vfr+EUx/AwDsQNylWP6tnMzOKphgDV6lmqqxyzjqtXGvWi/6tWGrUGpLA4nCsHsUsc/givlhv1JSbAFW8X44BxwDiwpjgwX2iDEeBCW+V5uznY9r2gYRCIiAN91TgMuOqArvKOGjI/OQgkGsAMuOoEK56Cyhq4e/funTt3kgpHOO2BvsuoMBF05hsLiPEz8cpFmbnoqDBgD9yiVdNgxOeWGJrzTNSl8bn5z5iXRRcOe7WIFwqX+M82mZjZmQsd8qTsmkxJ88LyVDzmjAPGAeOAceAwHFC7Jg8DoY0kBzvaL88PQ9BSBLdd9Ksaykgt6AtqwixFX/wYpQmEgwCnBsJZuEy9GANWP4BKNCCZW9CX3HjKjpKEg77cov6CqXjAYyZIB9BONHBXcwPLyYE4wYrkpWDd4fLQmc/BU8Z+hcmsUJNFas6JTVrgue6Z/bnJE/tvHDAOGAfWGwfavhMWMBnwFNRUJZgQ/Kq24g/2rgImcfqUCJpQo2m45qazt3iqt8GEZ0JUr9WEAHOrmkvkIE+N0K6r9CGoRbji5ljFQrIeuOo0YMZ70X1LDS8R8oT1tbr71y5CLF/jgHHAOGAcWLUcaLsGvGpr3h7CmjOqZAJWqCa/CNouOPvN/7viR9fcXCgKJtO1EDUfp1Zo9dvVOGAcMA4YB9YTB9quAa8nZrbWVdR31NsGS5FCXrXmffdHP+7pH3jyWWcAzQ59Xdcn7Gv5rSnNbxwwDhgHjAPrgQMGwEvbysBqUwkWE7QD2IZXrHpT+dJofh+ndPCYceyKV+kCfKOyA4k544BxwDhgHFiHHDAT9BI3urM9M84LY8NouvzqDa9U8eLdvYVqQ2+ZjYV1WuJwMR14iVvAsjMOGAeMA53BAQPgpWwn0BcFV3a8YuazG+hVxC1XvRLnMoWY9yx47JjurNNuyvdSUmB5GQeMA8YB40CHcMAAeMkbqrn3pANjELfW8Mo1rNCNaDKVyXvM8haQdk+dorzkBFiGxgHjgHHAONABHLAx4CVuJE48QgPWrTXUEs22G2yAFYr2pOKxUk3Ql4VZ7mAk1iQZ/5eY/5adccA4YBzoFA4YACxxSwG+IZl6VcUE3dUIcxOJe9lKfrqYiDSSRRki9tIAcL0UDsfdALAZIZa4CSw744BxwDjQERww6d+eZgqz/qiWqDe0g1OVxcAJDmSqsi+W25AyzlCwTMUy/reH/5arccA4YBxY9RwwAGhPEzWcqtvMm300dROuINi/FTg2ZxwwDhgHjAPrkQMGwO1pdYe0oKwuMgKAKYbtr/kvPnPGAeOAccA4sO45YADc9lcAJVfPhJgFwM2drttevBVgHDAOGAeMA6uSAwbA7WmWpgZM7gCwmqAVgH0rtAFwexhvuRoHjAPGgU7hgAHw0reUrEQKBntd9mqCxkvw7CdLX7rlaBwwDhgHjAMdwQED4DY2UzDxSvFYYNkAuI38tqyNA8YB40AnccAAeIlbC4OzLAWORhn4JWumXEVCXrFY5DxjDiTOZgsJtudAFXZP5aQkc8YB44BxwDiwLjlgANz2ZgeSWzRg7swZB4wDxgHjgHHANoJY6ndADmKYt7q3ZQzYAHipOW75GQeMA8aBzuSAacBtbzcgt2UdsNsBCzO1mwU9H6rbTo0VYBwwDhgHjAOrgwMGwO1tB1GIG7IOGCs0MOymYbkSFYBtSnR72W+5GweMA8aB1csBA+Clb5tgxFezBmTna8CowEtfsOVoHDAOGAeMA53DAQPg9raVasAKyZRkGnB72W25GweMA8aBzuGAAXD72ooTkWQZEucB1xqhWijMsiQZAW4O/ILNxv32cd9yNg4YB4wDq5wDBgFL2UBgaqjBwt+w1wh54QiIWwFlw97kdKEWjnqx5NR0HtuznMdQA4rDkZBbFLyUJFhexgHjgHHAONAZHNDzajuD1k6jMizqr2i8XjUUqntRpwQD0OIaobDv67RaGb3GAeOAccA4sCQcMA14Sdg4k8khJ1e1TsLSqM2DCmcSms84YBwwDhgH1hUHDIDb09wy20p0XZ3rDAA394VWBbg9hVquxgHjgHHAONA5HDAAbk9btSzwBXLna8C2DKk9fLdcjQPGAeNAx3DAALi9TdWqAaMTg8SmAreX45a7ccA4YBzoEA4YALenoQ6vARsAt4fjlqtxwDhgHOgwDhgAL0eD6XiwlmQAvBwctzKMA8YB48Cq58ByAHAwAlqpsCzWdwTm83l9RJA+KhQK+Dk9VyMRQT2lUontlPFPT09riF7L5TIe4I2nmooMNRPCq9WqpuIsXuJQRCsQtuaz9P5IRAp1k7BgMSRBSSKRyGQyuvK3XqvxO+SU6aUnxnI0DhgHjAPGgdXHgbYDMIioE4C5ciI9HAAX9YD6rq4uUAqUnZqa4hEom0qlstlsMpncs2cPMYmgGExahdV0Oj02NqaBeOLxOGl5GolESEUcMiSTgwcPkhzAIxwPOUMGRXR3d3O7bE4HgA9ZHDQfMtwCjQPGAeOAcWCdcKDtAAwiAjaALg6eogjiASwVRBWe+/r6CMcRoaenh+uOHTu4oi+CwXgAWrBTIwwNDWkgnomJCdISAQ/aMNCrOu6mTZuIrCVqckViMJvb5XdUk0KhTclbfgKsROOAccA4YBxYbRxoOwBrhUHHaFR23frRj370whe+cOvWrb29vRdffDHwjJ5K+F/+5V++7GUvA6pxr3vd68bHx9GSicMjIJlA9FdywPO+972PQPAbfffAgQNPetKTCHzEIx7x4Q9/mHAKwoHHIC7hhODUgwlagVADl+2KIToA4IAASFKqlo0MK8g4YBwwDhgHVhUH2g7AYCEVVp0VyzCo+bSnPe2jH/0oGirWZkARDCb8xhtvfP7znz86Onr33Xf/4he/eOxjH4uWrCrvKaec8va3v/36668HbsntrW99K+hLhiQnq8HBwRtuuAH0xX3wgx8EzlF8BwYGSKvjypSOHweEb9myZfm5DwBTOuWi/gLA3Iprdg70zq7GAeOAccA4sN440Pa9oMFCeArugn9Yhs877zz8gOsrX/lKsJNAbgn/6U9/Cj5hcybwwgsvPPPMM4FbNGCgi8ANGzacfvrp5JPL5bhVzfhTn/oUT7/+9a+jSm7fvv1P//RPv/CFL/zt3/4t2Kxx1OxMKjxkjkrNgPHyN3CrBgwZPgAvPx1WonHAOGAcMA6sJg60XQPWyjI6qyZobkFfIBP1lLlR3PJIx4MJZEAXt3v3biCT+VY8Yuh3cnISszP+hz70of/1X/8VWHH379//oAc9iFSovMD2M5/5zJtvvnnfvn3kCUJjiNaiyZyhX+IA9ssDf4qxrWUFJujWQCXPrsYB44BxwDiwPjnQdgBGSQ0gU1cTKWSCx2AkZmT4js6KvouHVUYEvutd72I8GOhlJJjAN7/5zejEN91004te9CJs0R/5yEfIEHWWmdLbtm0DyBXaQWjsz6Bya0MC3u9///tPOukkpnHhGHtufdoOvz/s3JCzjpS5zL+qN8JeqB726rWGnFEorlHnxEJzxgHjgHHAOLBuOdB2E7TagXWsF7/eApnoo4AoE6x4BEhv3ryZNiD893//9zEUf/7zn+dWR47/5E/+pL+/n9u3vOUtxH/Pe97DLC2mSROZhIQf0gG3aL3Ef8c73kFCUJ/I5HzMMcccMv6SBdbqIU4ADkXCIQZ9WYIlhxJO5GvovrFYfHw6z22t3oiEoNxfKLxkRVtGxgHjgHHAONA5HGi7BgwrQD5wl8lWqqqCuwzlMvQLRvIIJMYorRow87CYUXXZZZfxFHWZcJLr4l1wGkB9ylOeMjw8rPlwOzIyQnI8RNMkrTouJVIuEVCOMWhjl8a+3d6maRngdRqwKLl4oA+wDTEbOhR1XYY6CjGP5M+cccA4YBwwDqxLDrQdgNH8cGBhoK3iUUwFGgFIkJVhWjRgBnEZ/b388svxY0zG0SI8Am6ZHa2Rf/KTn6AWq3bLfK5rr72WzImAsfp73/veGWecgVGaVEzCIpyCuHKL+quNq8it/uW8BmPAgTV+OUu3sowDxgHjgHFgFXKg7QCstl9qrliI+vvzn/+cRUdA765du26//XamTYGpoC/Q++1vfxut95ZbbgFBlVk/+9nPmO0MDBPza1/72j/+4z8+97nPBZuBUtRltF4Ghsntyiuv/OQnP8nMalIRWadJa6GUi4oM8oHHwcwszbwtVwf2Og+LuivyBwDc2iFoPmwLFZapccA4YBwwDqxyDrQdgKm/Ig0qLPh3ySWXvOY1rzn77LOBwwsuuOCss856wxvecMcdd7BBB+DEbCmGe9lVY+PGjawvIi3riz73uc+dcMIJLEx673vf++pXv/oTn/gE9mQesRPWFVdcge5LbmSFYw0ShmuwmeXCFEqJaN54AHv1LBvmzVlsFACwacCr/Hsw8owDxgHjwLJxoO2TsKgJRmaQDzWUsVjU1nPOOYfFwcBtAIdEQOVFr2UWNOBKTByqMHOY2Trjl7/8pbID/Mb4rH6Sg7Kg8o9//GNC8CsqE4FsNRpoB+7yVAvCDxJr8nZfAWDf6u1KcrgrB0KIaym71d8SbF7jgHHAOGAcWPscaLsGDKZiYUYZBX1BUEzQOhMKUAR3ddkuT1U1VBDV8xiIiTZMKh7hJ7IO/epmGijQPAXO9BZE5ykWacVaPYwhGAPWzGnM2fDXntZtjjcHuYOyECDQ27oTVvDYPMYB44BxwDiwLjnQdg1YMRXoBWVxCpAgpd7qnCnwCaAFnwBa9FTQFA1YlVfsyXh0OrQ2EPjNU51ORUJuFdhAaEVrrNBsrUVk4gS6r6Yl5vK3MkWuSLnLX1Mr0ThgHDAOGAeOnANtB2AlRW3CAVkgZeDHo1gLWAbhiq88CjwaX2+DwCAhtmU1LxMSZAI8a2BrWcvjx9QN7tJRYIo2Rgb6BOFw3IuE0de5hTCPCC1G+OWhykoxDhgHjAPGgdXDgbaboFdPVZefEroUOhJsGvDyM99KNA4YB4wDq5wDBsBtaaBWU7eOAVMMMGxI3BZ2W6bGAeOAcaADObBMJugO5MwSkKwKsAKwgq8Yn80ZB4wDxgHjgHGgeV6AcWJJOTBb0QWAmY9NAQS3AjDwzCNzxgHjgHHAOLA+OWAm6La0u5qgWzVginFKsGBuc2i4LUVbpsYB44BxwDjQERwwAF6OZgJ6KSYA4KDI1qHiINA8xgHjgHHAOLAeOGAAvOStLKO8DXfQkWq6WJ9ZcFTndEKORKrL8qRGKOw1wnoesFmhl7wBLEPjgHHAONARHDAAXtJmkkMG5ajBSDRcrtWrdRn6LZRYAJyIRpKJRCqfL4K4EVYncTaErANe0tItM+OAccA4YBzoHA4YALejrUQJVvUXn2i9qL8oxTW8YXmGC9v8c2WEXY0DxgHjwDrlgMHA0je8jPiGBIC5kDt3hDD/Gd23DgSraz5d+uItR+OAccA4YBzoBA4YAC99KwG3AK+DXwFgXforM7AEjOVqzjhgHDAOGAeMA2aCXvp3QEFWMNiTlb4AMCFovFxVD176Ii1H44BxwDhgHOg0DhgAL32LtWq5AQBzSoRisMzLMmccMA4YB4wD654DBsBL/woAwAz7ar4KwPg5wQkMFiXYfyJq8dKXbTkaB4wDxgHjQIdwwAC4vQ0VgGygAfvgHDxob/mWu3HAOGAcMA6sUg4YAC91w9TrsXic0V803WqtGg152WyJY4ljzqEHZ7P+3OhwKGwq8FJz3/IzDhgHjAMdwwED4LY3lZqaVQNue2FWgHHAOGAcMA50CAdsGVK7Gkp2uXI7XcnM50YDJViW/jZXJWmpcm/OOGAcMA4YB9YlBwyAl7rZHcqSqYBrE4DxBpOw/MFfGwNeasZbfsYB44BxoLM4YCboNrZXU+UVDTgAYN2Xo42lWtbGAeOAccA40AkcMABuYyvpVpRqglYApjAD4DZy3LI2DhgHjAOdwwED4KVuK2d2DtYBk7tOwtIxYPx6u9SlWn7GAeOAccA40GEcMABe2gYLe5z164XZgpJ/HDrItRKKVOXwI5YkVSms3pB/nFnoIixt6ZabccA4YBwwDnQMB0ABc0vJAZC33ghjfA7Vyl5D9p2cKDYasXTCK29Kx1iMlC2KETocSXA6IThtE6GXkvuWl3HAOGAc6BwOGAAvZVvN21hDVN6qaMChaKiuGrCDXVcoBzQsZeGWl3HAOGAcMA50EgcMgNveWjrua2PAbWe0FWAcMA4YBzqKAwbAbWuupn6rANwyCxoLtLjmc72zq3HAOGAcMA6sLw4YALe9vQFgViK1rAMOzkMyDG47860A44BxwDiwajlgALz0TTNnk6tWDVj984aKl54Gy9E4YBwwDhgHVjkHDIDb20BgrYJuy17QvgZsJuj2st5yNw4YB4wDq5sDBsBta58WgAWD2ZZSd6ZsW3mWsXHAOGAcMA50EgfaDsC1mqyFxeGpVCrqz+fz6ikUCuopl8ulUkmjaUhwJVWQiQZOTEyop1qVdT6arcYJitAIug1kgHwAoYa39RoOe4z6ShHgrueNjo6mUqngPOBcLhdxxQPQti1lWxvCMjcOGAeMA6uZA20HYMVIWIANFlcssg9FvaurCywcGxsDmTKZDE/j8XgikQCYNQ4h+IFkEBroIpCQ22+/nStuYGCA5OTMzCY8OAKJMzU1pcWRllII0VN4KVTj9PT0uAzaeDkcxNt5wG1kumVtHDAOGAc6kANtB2BgFe12fHwc5gBCV1xxxXOf+1wQFP9ll11GYDKZVAx+5zvfeeqpp4KpT3va00ZGRgBp0oLQQOn555+Ph+ub3vSme+65h1QotWR78803P/nJTx4cHNy8efMHPvAB8FXjkyf5k1BbhEDig827du3SkGW6Oiu06yG0ngfsUwUNLVbqZaLIijEOGAeMA8aBVcKBtgNwNptFuwUjqTCmY5TRZzzjGf/5n//Z19cHIh44cICnvb29b3nLWy666KIPfvCDN95445YtW0488UTig6Cg7DOf+Ux030svvfSzn/3sv//7v7/73e9W3vH0qU99Klj785///BOf+MSHP/zhT37ykzxCDwZ9yZwrt+AfheIB2o899lhNu5xXCIDUYBmS6uLLSYCVZRwwDhgHjAOrkANtB2CAh2ozQAvwoPg+5znPeclLXnLuuecCk/39/WiuqhxfeOGFaMYvetGLTjvtNFAWhfW73/0uSXbv3o2i/JnPfOYJT3gCjy6++OKvfe1raov+13/9V0Z8v/e975111lmkfd7zngcMUxalqClb2U1WSgO3DMdqYPuuaoKm0KAIaoELADjQy4MI5jEOGAeMA8aBdciBtgMw5l/YCgJNT08rfzdu3IhtGT/aLVeU43379gG05513HigFPoGgZ5555uc+9zkGcf/3f/8XRfZRj3oUEI6i/IhHPIJRYSzJk5OTXJ/0pCcxp4lMUKNf+tKXAswovoRgvtbMtRQiUzojyq24yKPlcdQ90IApEX9gg26B6eWhxUoxDhgHjAPGgdXCgbYDMPBDXUFWQBEQBRdRT7FLEwgM6+jvLbfcwi2WZ7BJ7cZDQ0Pox0QGg7du3cpTNSN3d3dzC/Ty9JprruGWKVoK7SeddBL58wijtGbOFWjHrA2cE5NwiiCwrW7+JCw4gKMiwL/620qAZW4cMA4YB4wDHcGBtgMwqKNLg0BWQAgHEutsZELQj9FoN2zYALPAY0JQYUEpwBJYJS1qKxBLYDqdVqUW8FYdEtWZaKTiESFgOX4M2oA9aK1xtm3bdsEFFwDwhAD8d999d1tbBbtzyJOlR3V0Ww78bcT84hq1WKgRxRIf5njgkL8wi2fSOTFnHDAOGAeMA+uRA20HYJgKgoKyqKf4AWCuwCFG47179/IIDAZxCQRZgWrgEz8rlDBE8xS0xoAcJGEQF2Q9/vjjgeRjjjmGHDRbkBt8Ba0f9KAHYYIGmAnRR+SPugw249G5YOTWNlePhKuNerka8qoeABylYpPZyUTMi9eKfclYOBk9kJ0ShFYQ5sBgw+C2NYZlbBwwDhgHVjMH2g7Aqv4CfkAvuAsSg50AJOosejBPgckHO8c8LI2DLsvEq6c//ekwjulaBw8eRIUlCbfXX389QAv0gtPYnK+66iqSkAM5f+tb33rYwx5G/libV47jda8B+NZAVV/NbXgEeaFqzKtFCAeYQw1/DFigNxgOXjmSrWTjgHHAOGAcWAkOtB2AAUhAkaqpYRnF9Oqrr2bhEBowqu1NN92k63of97jHsTbp61//OoO4r3vd6xisZb40Gu327dtf/OIXM8Hq2muvBZVf9apX/emf/inToUHcv/u7vyPPV7/61ejBl1xyCWO9f/zHf8zqpmWY6nzkLaVDwtAJH6g7HtyRJ7eYxgHjgHHAOLBWOdB2AAZ1wB7Yh54KoP7gBz8AMh/zmMegAb/2ta99yEMeout6P/7xj7Olxjve8Y6zzz6bFUpXXnklqQBprv/8z//88Ic//JxzzkEbZgkT+3VgZMY6TW5s64FyzOrev/3bvyUHkJuCmMClavfKtpnCLGiroIvKDisgSQenV5Y2K904YBwwDhgHVpwDMuDabqdjsYAihmj0WiC2dUMMLNKM/mKO/tSnPkUcHR5myBYPZmdwi2nPn//859GPFcCIA/pqklNOOQV9OqBf520B20QIAlfAM3t1kQIwJB1CA54dcwVItSKNA8YB44BxYIU40HYNWPGS2oG+WkemROEBX3VWMzou6KtmaqY94wd9icB1eHhYk/CUoV/8gJmmIhq3ZK7oTm5olkyHZtIWGeriYE27stc5GjD0Q6fZoFe2Uax044BxwDiwGjjQdg1YlVFQB00XFRAHEus8LK0/oItH1UT8CqjcEpORYEVWYJU4wC3hoCxzpLEzE6IzszQ3HhECPKMBr+g8LKiY5bRqdAtmajrrud0YB4wDxgHjwHrkwHJowIpA4CKYCgjh4QqaKr8DD4GgFHEIx/KsY6VALOgLDOPActIyRxr0ZQBYk+MhMn5GjimICEA4kKxPV/Aq3QEZAPZJoHY4CcOtIFlWtHHAOGAcMA6sDg4skwYMslJfBVc83GoIflWRVcflFpTSRwqrhOBU01U/G1LiUTN1q0ct24QEOWv85b4Cr8x2prIUHPJiUVnTDP3UHcqpHbZ0eCHdjGrNi7BCabkJtPKMA8YB44BxYDVwoO0a8Gqo5GqgQfRf04BXQ0sYDcYB44BxYHVwwAB4qdth3sRmtcCjlwPA6veLnBdzqUmx/IwDxgHjgHFg9XLAALjtbaOg65YBC7dtFnTbOW4FGAeMA8aBTuCAAXB7W4kR4SYAyxA4fgB4Zv9JU4Lby37L3ThgHDAOrF4OGAC3t23mADCFOTx2hRr6tpf3lrtxwDhgHFjVHDAAbmfzNNcbAbpMA9cxYDFBN8PbWbblbRwwDhgHjAOrmgNtX4a0qmu/9MSFvZCwlIVGsrwo5JXlXMKI1wgnWJLUcEufORdYncSwDpDPDPunHAj6Zm55WjBYIe/J7EfNe1vGZq+OcaBjOWAAsLRNBz/l4MFYoxrlzN+IVwp545lKNJyI1cqbeiPRUGJqqhQPe1X2o3QjwrYOeGkboKNzA2Lpo/EDeOUcS/lxYrR4eKT3XAWJ+ZN/zavz2sU4YBzoLA4YAC99ewGtIjAbdY4EZkeuhujDXlSOBK6E5HBgufXwmfAURpib4YCCa4Ct9oLMsMZ8xoG1yAED4Pa2qghT53QdMIXNngRtBsT28r+zcudr5IUIfs7rX+ZWRCOJVmzOOGAc6FQONMcjO5X+zqAbCGZnTV2GhL8ziDYqV4IDzjzSOjXA76LxTzvLsyDXf7gShFqZxgHjwFFzwAD4qFl4vxkEGnA06p8zIbOg7zeJPVy3HABPBYD994MJfQEnZixV6iNKyI1iBDHMYxwwDnQcBwyA29tkLQA8sxFHAMC6O3R7KbDcO4gD8mbINAEv5HC20YrBCsczvTeJ1UFVM1KNA8aBeRwwAJ7HkqUOUJtzsA7YTNBLzeA1lB9Kre6T1sRgQWTnHNZifq4r6MpEP6cqGwb7DLJ/xoEO5IAB8NI3muxwFQhOlz2gy/mDdhrS0vN6jeXIa6NvjtOAa6HmnRsAdljrv1ghFrkJBsuxlobBa+wtsOqsHw44S9f6qW77aypTrFQi1liF5FWqXqlUYgp0NOKl02kGgAuFAuN8kXDEVOH2t0YHllBj5ZrYoTktGl/ZrWSbLvm46+oTTMOSY6fNGQeMA53LAQPgJW67Q27wjO4LKOsjw90l5vgay44eXLVaA1qj7MHhgbxc44nmhCu3KYfgc1NVXmO1t+oYB9YVBwyA29bcDm8Z0QNxFYDVCs2tKS5tY3rnZxwLs4NLOCbq71TRu/znt92zPw8GizlFnf/2yJcb6MLNZ/bfOGAc6CQOGAC3rbWaAEwBsguHG68DibFCq9x0WnHbSreMO5EDYidB940At7mqd831d3/jou/+7JrrS76+2/xamR1tzjhgHOh8DtiX3LY2bAIwGi9bcMBo1YADAKZgU4bbxv3OzThc80IyDhzx9o1P7dp78MB4Rl4VxWCgV32GwZ3bwka5caDJAQPgJifa819N0A5/ZwCY6VlsFU2BBsDt4Xon54oGHAkLAIe8Wiia6Okt1cT+3GJttm+2k9vXaDcOtHDAPuYWZrTBy3ivasDkLVqwA10bBG4Dp9dOlqFwrOzOQpqYzjXi8WKl0jRBuzpKzw2UboXktVN3q4lxYF1xwAC4Pc0tElKcm71aZ7gXRmOTlnHfRkN3M3J7KRj/lU929TlQK5c5SysiKOsNT2Sn65FqJBaXOdFeSAJZwhaTM6dDrftF+2ntn3HAONBZHDAAWOL2apoKMSDWYG5uuphMxL2GrCtJAsAeKk28UMAAHaoSFG4C9RJTYdl1Kgci0XioXusOC+JOe8l8KF2oisKboEL1EltUMqGgwcvltGA9uaFTq2p0GwfWPQcMgJfyFRAVRf6cdHT/mXKF4hJ1OAuv0YBFAdY4YkU0AHZssotygBeDX70RkQOlvVI9Uo6mynUAl/Cq/NjaxaEvT1GUw16VLbGMecYB40CHcsAAuG0N58+CFvnIMiQtRpce6XlI7nnbSreMO5UDoC0/2eWKPdR4YapVsZWYMw4YB9YeBwyA29umtVqNSVgcBqwyVI8Ebq4Ebm/RlnvHckBelorn5QtFhnrLlWATjpmv1SwnHdu4RrhxYIYDM5/0TJj5jo4DvoXZabgAMJnFYjHNshWATYYeHZvXYmrVfp36iw2abcNZklQusyE0JuhDfKr2Cq3Fl8DqtI44cIiveh3Vvs1VRZFRZXe+BmxGxTbzvjOzl302+MnbwWEM00VM0JHKjAbcmZUyqo0DxoHDcMAA+DCMOfpgmW/lA/CcMWDbf+Poubtmc2DBWijMxAFeHtF9Q6FKzY0B+8pxa70lVuu9+Y0DxoHO4oAB8NK3l2+CdjJUNWCde0VJbhY0+GsQvPRsXzM51t0SX9C1UuUSrssStkM4sz8fgikWZBzoKA6sGADncjllFFM98ehYKbikI17cBjOVmAUaRNAkxWIRD+G4Vj+B5KYh5KN6J/n4o2iCiIeWZZrtklzZe5IdrxoV5tAwfudVKhUKhRKWdRLU29vLeDB1ZwFwTZeXLEmplska4gDvBa/p6HgtkUqzaWmxUgaHzRkHjANrjwMrBsDd3d1jY2MwNJFITE1NnXXWWWiHzFHidnBwkEHTv/mbv5menj7nnHOGhoZ4RDjQ9Qd/8AdAWjKZBGgnJiae+tSnplKpHTt2vPGNbyQJeEwOPAWD2e9CQV1DtOWWAYDnvCJa4nwNeE40uzUOCAecnZnZeyAuL7OEyNyruR8pseYGSVRzxgHjQIdxYMU+5EwmA7LCLdTTvr6+Sy65ZHJyEtVwZGTkP//zP4HPpz3tael0mqfPe97z9uzZc+utt+7bt+/Tn/40QAsGA8ZPfvKTSUjgBz7wgSuuuOKCCy4gB51vrFcA2JdibjIUc0pV217OJlI9XnVxeK1IPAeVl5MeK6sDOOBOPGINUkMGgwOjjZuf1QHUG4nGAePAkXIAy+jKOMCVgsEnsBbPscceC15ms9n+/v6rrrpq586daLeEE40I27dvx08EAAwHvv74xz++8cYbL774YjTjV7ziFcD2xz72sXe9613EQZ9Ghwbk0H1BPkJUaUZXxpHPMjiI1FIoHQ+dBq6iuLCPIFth+ScCLwMhVkTncUD3J8X8A+m8LXKcR+dVwig2DhgHfjcHVkwDBhrRd4FGFTTYkwkBfcGqj3/84y996Ut1KJcafP3rXweGzzjjDHRcAAzVmcAf/vCHp5xyCrAN1nL7nOc8517nyIQQFF8gkKy44gcFwTyA+dprr/3dLFm6GMhNikaEqkYOrwMA5lETo5euPMtpTXCg7kzQ2XxBpkML/LrjgQ9RNV4iGx0+BF8syDjQKRxYMQ0YgzDDwLAJFRaUUogChi+99FIMxe985zuBTyATJH7ve987MDDw29/+9vzzz0fT/fKXv0yq4eHhM888E/AGm7lu2bIF6CXOqaeeioe0ZELO4+PjaMYf+chHAHuKIJ/lbBg0l1YNmKIDAF5OMqysDuKAaLsc2cFJDC0asA+zvlWlg2pjpBoHjAP3x4EV04DVGgwuQh2yRsEYgPzUpz7FoC8givJK4Ctf+coHP/jB27ZtQ8e97LLLvvKVr9x1110kAV9RbQFa/GAwcI6iyZCwwjaBQB1XtOG3ve1te/fuJT5g/NOf/pTAZXMI02AMGJGKU9M0gWZVXLZW6KyCROV1im2hWEYVZha0XE3R7axWNGqNA0fGgRUDYOATHAJi8bA4R6lFhWUA+HWvex2KI+HAMGorDogl5JGPfGRPT4+akU866aRdu3ap9kxMHIirKI4dO7D6ki3JAXsGknnK0PKRsWWRsVBRWrSUKCe7VhmJ9qrRUFVPY4i6QT3Ol6vJ0YQ2l3WRfF7DyXgrQFtWrHEKIS7MaVp1mUfAC6PvS8sRhPYGreEXwaq2LjiwYgAMd/P5PFd0VjRgBoPxf+ELX9i8efPTn/504Fa124MHDxIOVBNy3XXXMUvrEY94BP4nPvGJN9xwA9Oe1eD81a9+FXxFVyYyThVNouktcdQThOjt0l8bHHwEuQ0vHGMDX6qULYdjXjVVLxKMvOxORVkcPF2W81xFJzZFeOnboKNzrIdDFZCXpfFj5WixWE6wF2XYK5bkXXLvlseL1PxoMf+03HV0vY1448C65EDzW172yoOvQCYzrZhUhYKLnsqy4P/5n/951atehS7LLUrt7t27GfH90Y9+tH///p/97Gcvf/nLn//856P1Quw555zzwAc+kGXBt9122ze+8Y1//dd/xdSMnVnroZbtZa+TK7DByhEBfg5xFQwORSKNelw0XpGboUadc+ZqoTAxCLHDXFemjVZvqZwQLa8KcFtkyr9s5SIvDLPmXY+NXSpRj7FJB27Fvt+AAvMYB4wDi+bAik3CQnnFVsymGTioBzsZoAVNAWBuVWdFM77ooos+9KEP8RQb8mtf+9o3v/nNDPQC2+iy3/rWtxghfsADHsAIMfOz/vIv/1LzCWzRi2bKEiYMxoDJUzDYliEtIXPXaFaKr7zkGHIYqWA8pWnKWaMVtmoZB9YrB1YMgHXcF3xStZVhWkAUP7ovogeLNPox4MpyI6QQcAsAsymH2pAVs0844YQrr7yShiMyqTBod3V1qeGaaIHZeWVaVo3gzWVIUCUGZwPglWmMDitVAZjeJ901UX7lLeqwKhi5xgHjwJFwYMVMWFiY6dqDnUAvDnDFgbIgF/jKZCs8IDHQBZSKJEKDDIWIiYfpV1yZ28yVpUqgL8uTQF9uGSTmusLo2xzZRXZqj0G7BRBGFag13Q7FY0LMGQfmc4DXQwFYXxg3QWJ+LAsxDhgHOpsDKwbAirUyfdntecttgFIBR3UoFwzjkSrHLCXi6XHHHceV7bHAbyzS+PUKtoHc3LKemOuKO8RoAMD4cdqTMABe8aZZ5QTwtmDR0bcFUtnNZZUTbOQZB4wDi+DAigGw0qrGZFVbFYkBURRftGEibNiwgSvqLECrc69Y16uRwWOwjchEOHDgAPjNoPLo6Khmu8wbbmihM9emBgxxrWPARECkqgY8E9l8xoFWDriXhzdHx4D1iXbjWmOZ3zhgHFgDHFgxABYt0MkatFuGe2Gl3mJzA5UBVB0bRgzxiECuqtei45KWEIBZlWZWLvGIKV0bN24ExVdyCvS8N0IrFagy+lwD58W1AOOAzwEAmE6nvva8LdqNM+4YB4wDa4wDCwZgdNP54mARiAImKYjCUBU0quMG/NVbVZEVaAO9lrQgNDEDYAseBXAe5LPcHnoVbn8ulnMyw40tuugowB8WbFYaHr0EblHiF8z35a6GlbdCHAjLErUSO2A1GvRBjznmGL4O3qIVosaKNQ4YB9rIgQUDAciHRGCGCGNUAe6aiexwTaQs0h6GrClxk7ACvh0ulYWvcw7orCtmF+qbYy/MOn8frPprlQOLWYaEUGDrjFaOqIbaGmJ+ZzWfNQaMGd3kqb0YR8IBdpQBdDH/YC8hvnVwj4RpFsc40HEcWLAGTA0ZZ8U4xhiVdszxoBB3XM2Xh2CdKaYdFHiNzRymzbfhLw8xVkqncMBt0uqxsk7HLwyAO6XhjE7jwII4sGANGPAAThRRFF0wlOEWVOp6iKwasIpO+MO0GjRgA+D10PRHX0dGeOioYWcKPrSjz9NyMA4YB1YbBxYMwDqKSTXwBPYxlGCdLbXaqrfi9CgAqxhVpqkGDB6bMw4cjgN6TgkAzFZYxDEN+HCMsnDjQEdzYMEmaICWQxEuv/xytaNijgaJDX1nvQRueRUaMCirXNKeCnF02raZoGexy27mcUDX4PFZqcnEAHgehyzAOLAWOLBgAL7wwgvBj3PPPZftqN7znvcMDw8jI1RerAV+LEEdHEt1P19/cXMdFmlAWHaiFEc5/JkevAT8XlNZ8PJE3DIkeUnS4UoiVOLwo4oXFUUY13xpmu+XhtrVOGAc6EgOLBiAOTLha1/72k033fSmN73p+9///sknn/ykJz3psssu0x0zdAcrNqXS3TA0sCMZs2iiBWr5scpZ/jVqcpQxhkQmsxbLDY6gYM9N5qzxCDmLFX/R5VjCtccBzpGueVHOIjwwlWc3mqH49KZkuR6OTJacJZoKCwBz3qVWnR3FfVxee6ywGhkH1gMHFgzAaj7lJKK/+qu/uvjii9/5zndyhiAK8Yknnvh3f/d3rBLWTanY3AoYXnemaYFcfj5XxSvKbiNCuMAt23G0qMAu7np4yayOR84BumX8imzaEqqnI5WEV6w3QpwqPVsD5k4DDICPnLUW0ziw6jiw4ElYOop55513fulLX/rmN7+5b9++ZzzjGW984xs5m+iCCy747ne/e+ONN6pFGm1PDzhadZVeLoLA3tYxYAbLcRSuJujlosLK6TwO8AXxktB/LbstOWwMuPOa0Cg2DhwBBxYMwF/84hd/4Bw7MP/Zn/3ZC1/4wqGhIaZiPehBD3rCE56AHsy2eczhJJDSdcPIIyBjbUYBgFtnQTMQrHNqFJWdVrw2K261OkoO6Cxo1gHn3SaUBsBHyU9LbhxYnRxYMAAz9Pvc5z6XqViPfvSj9XReuupuJXBs//79PCUwCAdpggnAq7P+baFKzM5O020CsK6T5lDjVgCm6IbXcCPFbaHCMu1cDjBLgM+KcZyJyUlqYQDcuU1plBsH7ocDCwZgTM3AiZpSNV/8CAiAloFhhoRZE0yEkZERYFiPObqf4tf2Iwbo4AySVNcBMzIMr7ilXwJEmzMOHI4DgQbMm8MLYwB8OEZZuHGgozmwYADOZDLgK+AKiuDnylgvtjId7mXiFRijJwN2NF+Ohngkpuq1aMLwh6zUDIBSrCPoGng0RVjatc2BYB2wvjkGwGu7ua1265YDCwbgHTt2qHQAhgVp3KwitF6WBbNBx3vf+15YyeojRAb7+Kg2vG6ZS8VhEVflEgCsGrAGrme2WN3vnwNMquAlicWi+ubYC3P/7LKnxoEO5cCCAfg//uM/WG70hje84QEPeABH9rIg+EMf+tCHP/zhAwcOvP3tbwd03/rWt6LhoSIzjsVth/LlaMgORcMAbz0USoQ8bImq9ZIhS0vS6Tg6DT0Ym4F1NBxew2npr7E2fGxsLBSLDQyIyYSerlqk13CtrWrGgfXJgQUD8Oc+97mPfvSjL37xi2U3iVDo2c9+9imnnPLpT3/60ksvPf3009/97ne//vWvHxwchJug77rFYFRdfZ9UdwkwuDVwfb5wVuv75wDvDYMWvDZ8XMzaUxO0jVncP9PsqXGgQzmw4I04rrrqqkc84hHYlsFXhn7Z8YoFSGyJBdY+5CEPueaaa1RkcIsQWZ8acOuroJOw3KGubI6FQiOyFXlqk7BauWT+Vg4UKzJ1gO8oGpGFfLwwWKRbI5jfOGAcWBscWDAAn3rqqR/5yEeCWSHMc/7yl7989tlng7V33HEHE6H7+vpgDbcsCNadKdcGpxZQC4euqgKr7hJ1hgZCUIwVgBeQm0VdZxzgMGBeGyzPnPHJKI8B8Dprf6vuOuLAgk3QoO+znvWsn/70p2eeeSZWsltvvZWtr77yla8At2yPxa5Y2WyWHXyQHujHCj/riJ3zqqo9FUQpoExnxzTgeRyygLkcmJ4u8mWh+/Jx8h3xEQX93blR7d44YBzoZA4sGIB/7/d+b9euXZ/85CfRd6k4t1/96ldPOukk/K997WvprSM78E9NTaEKzxn77GRGLYT2pgbMfxWdqgGTBQCMQ6S6KAvJ0+KuGw7oxD10Xz4kDNHytri59OuGAVZR48B64cCCAZhzF0Dff/iHf0BMICN0xJcp0PhZ/gv66oLgnp4eWKj+9cLLefUEZcUGwJlHEf8AGzNBz2OSBczlAJPkdfIzD/igQF8D4Lk8snvjwJrgwILHgLEz6yGDWJhBXwzO8IF9oUFfrNDMFsF0hkd133W3FzSQKyO9gK4gLtNZOTou1KgJ7rrXhVG9aIPlSJw654esibfIKrE0HOCF4SzCIkcShuuJUD0ub0u96oVqsjSJg7QwofBqyT89cUtsTQt0JNZfM11wuKEfzlPfiU+2kTFnHDAOtIkDCwbgF73oRdicsazqzEw03WCRImt/dc9FPG0id7VnC/Si9Hq1YrWBJM1VPRgSC4ciCEzPK1S8zV1ej1ecmi4BwEQNuy2jV3uljL7l4gBznXMsAs5XGo3SzoEERqR+OrmprtHpsnyovEOgLxi8WEcGvJb8yELgVf77P26DG/fIPeXsYQk2ZxwwDrSFAws2QWNq/pd/+ZfvfOc7TMViMRLLkNiKEjxmcXBbCOy8TEVgcZqrSDRfktXRVFSMqQZca0QQtYtQXzqPGUbxQjjA+8KLUahzoHQ1GaklOE/Mw34Srbh5FS6nFjgklnT4FuCIra+k/2KSVHwEH7IjvrDMF0CHRTUOGAccBxYMwJz4+6QnPQnLM0t+GfplLvTPfvaz448/3vgZcIBxOwVXdGFG77DG6y2hNgkr4JJ5DskBXhUd4sGMBDgu7WEMwKz/KjY9QkPokOjrwkUDNmccMA60iwMLBuAbbriBIV6kQ+s+z+jB7SKwA/MNALiGoRk5xpZGrhagr+7IASqbYOvAhl0OkgFINrGhJOZYAMAsQ+JtWcKNOGQwuVXhVUCWTWJ8LXjWmylv7mHg2eVjF+OAceBoOLBgAKYw0PfXv/41g1MbNmxgDBgT9Do/dnBWA7BipKnyupOQuFO9V9RfABg8rnJG4aw0dmMcmOGAAjDfFO+P7oS1VOuAQVkHwK0g6+Zz+TAsNCje8n5ir5mhyXzGAeNAGziw4G/s3nvvfepTn/rQhz6UjSfpmIO+bP7MCuA20NaZWfrHHwnxAQAj35B5Iv5U/tk64M5s22WgmpdEAZi95HiV2MKFlWxLpwEzKFJlWNnDBONbYWYmAlI0v8BZHzFghXmMA23iwIIB+F3vehcSYWJigisLFlnp+4IXvOD6669vE32dmy3ySwFYLdLcwmsdDcaoaNKtc1u23ZTrGDDbyfGW6BYuS6UBO8p1Aj7TBOV95D3UX1CpVhi2tzRgi3mMA+3gwIJN0D/4wQ++9rWvsep348aNWKExkXFCMLtRtoO4jsxTNWCVaw2ZhKW10H/a3zEA7siWXS6imV1BUW70V4Yt5G1pvkVHS0Kg+Dr0BYr1tQR0eTOd+utbp7E/uwkMQfjRlmzpjQPGgfkcUESYH37YEGZggb48VjFB3zyTyWCIPmyCwzyYnJxsfcLWWmiKurUWntNOO42nzO1iS8u///u/Z0IKA8/nnXfe+Pi4Guh4evvttxPCUBmnH775zW/W3HRjEPyo5vQP8KCp49enSybINLvfdVWxSXWISE+Hf9WGjOpBUqEgiZdKrv4uQux5x3AAROTTAH0xQcfZQrzBsIUcID23AvTtMLAs9gWqV3gTZWH6dMUbz+lkQUpwdumWCYIKz3OLtnvjgHFgiTiwYAB+8IMf/O1vf1v3ugKDkQ4f+9jHGA9eKD2gOCNbgLcmBGs5UglMZYyZnT2uvPJKhA7gCjD/x3/8B8cdXn755ZjmWHwMEmuqF77whaRiEdSXvvSlL37xi3/9139NVkwKU5QF59RwxxQorHlaygoCsBIAFiseL1ZyajZ2Xcsc4L3FsQCJHps/f09V1dl4qC/Sghnh3jzgl71gwjH5pbsjqL2+5ivZCba7fwvO2xIYB4wDC+LAgk3Q//Zv//bwhz/8iiuuAH1BR1Yl3XXXXXv27FlQqUQmORJEVWf86L7A7bHHHssj0H1oaAjdFxDl8CVK5MgHwi+44IJnP/vZgDRnP1xyySWcBvH1r3/9mGOOAbnf8Y53oCj/0z/9Ex0CEqqOjhQDcXVzLh2xPoQmsVC6f2d8fw2wxFPNQgkI0lFrohgABwwxzxwOYLDh1U0mxX6jJmh6bUs2b56jheOJSq1RZfZVxBvJeKmE15cQU3NELjpC7OYKOlT2fXNItFvjgHFgKTjAJ7cwd8opp+zdu/exj33sOeecg733yU9+MgqrGnsXlBGKKakASKARgxtIjC7LntKA6ytf+cp9+/ZxmNKll14KML/0pS/VnJ/2tKft3LkT0AXSeHT66adzODGiiqevfvWrwd3rrrsOv6IvOWsRREB1xo/qTP6aVRuvTmRq/nqMeisAO50mDAI352e1kRDLuhM5wNtMf5SPoqtLyNd1a7zMgZlYQlGFeZNaunoSeCROhn5JyUZsYXTf0WnvS/990fd+eDXbb7Vo16i/LXdHkq3FMQ4YBxbFgQVrwJSCqvr2t7+dfjpyASUVeAMsF1q6YjZWaPAJ7DzjjDO+8Y1vnHzyycyvRrF+wAMewEgYOi7YHGRONCzM2J9JhZAiHOyHGIrev38/SvPdd9/N+ihuIQmzsyIfkcFgJBrdBU6SWCidi4hPWbreSLV8qtCaiat4VcbvWkPNbxxwHCiW5QhLGTdxuiezoKWfWq1IZ675sWI+WQT4KoMboQgbw2AXAmZvv3f8J1ddffyxxzzvWY9k20teyJAMA7uIssnlgnvn1obGAePAgjjQ/KYXkghcZBPKsbExpkQp/gGWf/zHf7yQPCQuEM4gLlOogFK2t0TZBbHA1x/+8IeEXHjhhei7YDwxAVq0RspCGHEFWUFTPEzOIgkIR0xgT0GdWVpMYMHaTIZQyBA1dmwKItrAwMBCiVxEfAfAkg7s5wq1Ada2asCyUzT35owDLRxgdh7vfCwe48us1jkWSd6fUrkkr1LEf4+IwFyClkQL8IbC5BKOxD2mdd11755GNJnJl6bLXhdHLwVO0NdszwE7zGMcaBcHFgzAF1988Ste8QrwDPBjsBZcRNfcsmXLQgFYYRL0pWagqWInfhRZMPhhD3sY9uTHPe5xYNjw8PDWrVtF6IRCTNFShXL79u2/+tWvIAMYJg69AVRnxoPJAfTlig7Blfzf9ra3MXgMkeA9Y9U6v5pH7XNOPkr2ah4Pqqbik1rg6FdI12KRUrR9tFvOK8yBfF7ecwxL0CHLkRICwIV6fZYJOqBxwYoweB6TCdAhOfVh194DiZ7Bcq1aYnOOGQD2dV/+mTMOGAfayoEFW5ne8IY3/MVf/AWAh9YL8qFoAp8333zzQqkEJhWfyATDMnCOmgu4kiG3gCsQ+4QnPIGQn/zkJ2SOVGK7D8pi1BmEZveP2267DdmEusn1oosu4kCIs846C+MzkYmmOI0fD6O/SDSgGkWZkHa7AICrjTqqClNN0SYQeirYmPvScEPAi5xWQ0b6a1YjCMAz44LQmaCV9VH7OT+hZx6ZQZyVpXZlSqfy09VGLZxIhkMAYq0q52jFvXqozv5VnhwrLYppmBOmfUPxIqzEjXCxUOGz57dvIj9Vje/P1otienZO9smiTeS5xlkZRqx4qe5bYjxeJpjg5DXVN5N2gEVuvZYL1ud2NQ4sjgN8ZYd1CpA8BtVQH/Gg8qJEsugW+FTFDlwkHMg5bC6HeUASclD4JApK6o9+9CPmXoHlzK5iIhVIj9p6/vnnv/vd72YZ0q233spMq0c6B2EsfMKde+65KMo//elPUXOf97znYXPGoVsD4eQZ0B/ooEHIYYg6+uAww83xRAoJiaQs1as1rxap1xgE5vOFRwR2xRPVei1X8qIRXe6xkEKFzfrxqziQPPEhJvS+2QzuTsVEM2ghxSwkLvkHv5Z0GtakS6lzlHJQsvzw+8TrM5fUVc2Xbn6ElizXvhfpPlwIFbz4zg0DHKk9kBYUjJbL3bFYtuRVol65gckqEorWvEoedsi5hQt0fKndXTFAhLJuH85OJTbXh4797T55f0Qr5sUVrxi7GTjht95sNA2pvv9KwyLZEkVu616dTg937oc5oibR+INR5owDi+bA/X3ACrpcAUI0yNHRUVTJ17zmNd/85jdVvzx48CAADCoDewulQOdSof6SkHFcBpXBYLReVvo+5znPYQCYEimX4VtuX/SiF2E6Pu6445gCjaLMUC6YyvrgTZs2MesKkpg4/aEPfYisgFg6B5imF0rPUsX3P0j3j6lf/I+FWO4h3yo/EZ9OprFAapElzk4XiICWYDePRu61zEWW065kPr5K9kr8LMoluCVAbteRo+b5WshpwBhO0ENFA44x3tvwKg2BTLnHSfdFPhz/1oUd4UWN1ryHY2PAeaQYSubqsUls3JKe8vWdCbvbI8xy7USbefPwNaRfy0+cPhC2K2PcJ9YM1ih2NQ4sggN8iYd1irKq3QJsevugBz3ogx/84G9+8xu0T2y/hIOIaJysETpsRod6oHObAWDMxST/9Kc/jRWaiOjEuCAFg7gf+MAH3ve+92lZgS7L7Co0YLbgYF9MwFvXFxGog8pYm4McltmjJgEVc6pwBzQrJUTAwVWZyroI/YIkIg7YolB0FjpQM8xq9TKPRqXGMtd/dnHM94FAV0vt6tW9kHvlmIjbjMnTw3UDF8GeZq6d+r9YlE2v6HoGFeDNp486d9r8Yl4dP0vp+0W9e3cP12usQaizooD1BQ1voFni4Vqj+Xw9/HcfV0tF+dzqWOV5n5svs3GphT3mXSwH7g+A+fIBV3BRB1aBTLRh7M9c0X0/8YlPKDaj/qLCKtgcORm7d+8GNVFzdc4UpZAtt0ye0vlTupQIhIYMQnS2M+Wi9UKVAq32CRR9ia+BAQwfOTFLGFOJ1wwhHoxVIjVEwIbDCmUdMBJ1sd/wbFwiFwxkmlfzSQsoL2HdFpkVYkvo8SWXT6Jf9ybBmrWryrqzes5iq+60yucgMMmGLf6m0HlnK5K9sY7WMdzrdLt7du8J1crpVCQzIUsPMHhLebJQeF073lTsVer0dZR2EL4LZzBDwyOJ4JpGo9nVOLBoDhz2ewPwyFSVUSCELjnTjAFI5l6BlCidPAKAwd2RkREnHRZGA/OhyI00AJKWovogedIfJ1yVAOQRyKowTCDoyxXxpJO/8OOwgROB+ORDuMKwPlr+q9ZC9RPlodNgZghBIfYBeJEoSZMFrYZyKT/uERFuJg2ZtuQrgmMZHbKKn3OOnqBoTJqiPbT8ZqhU4n2yAQD9BUnXmUdHT7RXqlXXDhxveJO1LnixGrAM8rop0Lv3HYiGqtuHejmgEADmpXHmCvd2hVx/bplfntXS0MHHJd/UjJOuCWPvsRrWA7HizESbiWM+48ACOXDY10hhQ6EOzAAXWWgEvDEMjET48Ic/zK4XBKrChzV4geVKdPAJsYJrNdUCTmqddr1yDz/ISimUpTit4ajdCCmgl3wAcjLBo8TgwazNdaUcpGrRStV8DZinxGnGWgiZs0TCTEIX3IK78oSW1d9MtBXxaY8AjuiIms5j8YfWHImHIn5FKF0VhSoA+xqwo4j3h3dlloVpMa+OXzuyokVoheHRiVSkcepx21hqrF1eMcmA64d5x1YFd9pPBC+q//W6CWit8hHll/d2zmfWfoqshLXMgdYXbFY9VZMD0vTLB0uYe8WHivoLcLJZFTKCONh7iaB7QM5K/7tuyIfkwDwOEYNVmbKAWHIDVvEE20RTHIFKD7kSTj8Aj/YGNKGqvwRyy1VnQeNZEYeMU/1EWSfdF0eHXqkIjgq2jHQvhEzkY/Dz0ynG6Y0DXemtO1VSrgvJfBFxlZhZCVvpkQdUXIOQX3SU+OEhECoddYFMc2RLsP7m5jOrkDV6o8M9av5R1ukcAt6lGdid8S2YC7JTqucdyHsjk7muRPi0E7dj1x6bYAzYAY+8ObRJc3ZXu1+eBZO/jAmcQcBnAP9C0mvh1RXDoGNS8NYuI01W1FrjAK/SoZ2aT0EJgBb0VTUOsARd8CMUgF7iYO8FldWYfOiMDhNKEs1N1UTNgQzJGSUbMFakpyx1gBaBRCC+TromGlANeaoHK8G615WGHKbktgfDIi1DPZDdWiQV4TaI0/pogf75EkALcpZeeuvut8A8jzq6bKLkO+UCV4JUvoO7qgEjyJrxmCzmfi6RRtOEzWzW1399jfk0gmrr+xP0147qzYGzdP6YAp3zMoVqKuods4kRkTpqN09ci2BfnfW6BmSsEw9M8N9M4dPMXdB31O5j2/u164Td676av+Nj048/EAfB0BRfLFom0MgY7caNGwHLxXESNNLM9dq6nAlIJv852RISEMMjMJgrGMy1Na2Gz0m7PLfah2ChIBIN7qG40KVwHWj5ZhnH0627GEpPxhhiXzBRZMtvxgl6SVAhLzuQ5AsVJEWhIisWkRSZ4iJ3+5jJ/0h8rQT59DEYL6MDdEWKJRm8pKIQds/+qW9des1ExTflSfXFIMokLR5KPbjh5zsfmGcCmg/Wwv8AUKkMmMotrwpV5f3Bz8eFZbhcKfN9qgaMZoyqSnNKB44uHYcEF9k/YxGuXql7u/YVSvXwyTu3Hr+R4iv54jRvD6U7s2u04j7nUqkiq+jWrRNe+JYA2JB3K6c//l9f/8I3L6Vry5DXeubNun0plrzihwXgAOfAWoy9yIgNGzYw3wqcI2THjh1MSCYQSEYV1lHbJSeuEzNUBVcpVyE7XwNujbPYOjpYEvRSF05JLyRc80JAWTgWmih4Wc6dS0YE2drqDiGHpE8Qdd0Ohs0iEVHmilWPvUeu/MWvfnzVL669YV+uIv0DZxog/cx8cG7cfVspXhWZz3kHuOU9AVD1Wwu+PmjVmPMV3zk5HFGt0OpqFdi+b4LWCG3d0EsHNpUQs1a+pEMDzPAlYwipc1qZjqQcUc5rIpLrggQ1YSYaG53IJwYOc3gU417X33LHb266dSrnVvS7LksQ2zzGgUVwYMbSNScxSi06KJ+irqlFYwNoX/7ylwPDhLOI6P3vfz8Drqik9Nx1EsecHNbnrYpFJBcyDM7ABNVglBtOe5FOj2Lz0gg4pARd8nJD5mZG4gDbfcPT37z4O4Obtr78vMfPtSEsdasAsTi5zEbioNsRjsqTBLs41b077ryH/Yf3jUw0IttcOlLVVP2FTWr8ROAF60DmZrrUxK9gfvqeBLDqvzZsRTk9TWAwBgyFAsycB+zepdYXRpMstAq1aqURTdy55wCbap1w7DaWG/d0paZy0+OT1W2bo1VZYyPFabkLzXyNxXfLCuRl1Fc7X/H2j01mMtPTJTYpazHVrLFqW3WWkQOH1YDRdMFgZIF++Ux9ev3rX88pgaAvJt/zzjuPAxJQfNkzkgVFL3nJS5aR5lVdVKtYZLiaW7SZVmxSeRpI3kVVJlB8g9RgsJynHouLdffe4bHLf/GbX153CwMDrUUHsZfeQzEitCHMpw2NTkf3lQCuqbiXLZRTPYOVUDymmgUpBFfYncOHYxITU3/LRvvSc+OIc+Q1wLW+MwrAOqqi4bw/eJSZzV6LMLU11REXKHBO4jv37K9HYsdt28JCwMH+bpKPT05w5VHTHFGnTbQ5jjzztRFTmDtTdXfn5i40Il6qeyCS7BG9OOSV225cWhvstFrcHwcOqwGTSEUDg1JgMLjLjlTB7GIMZa1jroseA74/0jrzmYpFBRQVmhgJ+IhVdHJVAEYD9r/sBVfTR7gA6jTrSMyfpYmhLF8PxTjlJpJEG15sKQsma34Caqq0ojowml9ueOMTU7Vw30QmR8/AZwocgVnstdiUefhI1VSCW7zzC1hzIdSdsV5eoa6W0wHVHO1rwFplhtabXZaF8iAUi9AcB8azye6+TUM9fP9DAwP37DkwMTHpeQwIywuDCqzZ8o7qzk8LLaVz48uL6DvePbE9c4evXPXYjtuLJRmvZzyFaMjFOGYbc8aBo+DAYTVg8lT8wBoG+gLDir54eIRs5QqKsOKWaHp7FGSsnaRIT9jSCsDBeJ5+2wEA81U3Bd0iqk9q52Q7fmkLBARBdMoB3Xv3HASDc1WB5GY8P/rS/tMa+UXMiC7gQU5bxFEcsjwaE4HF2rFCqVqq1cenpp3a63oO7kVyevDSktYBuTkOzai/vDZMjwJoGbPQnSi1M6dTEfVj9GuljF3s27N3eCpfqQ1u2tKdpAkaGwb7oWR8YoI2araheMkekjqAj+0lUTgAO5iWPpn1cgWOkqrmCzK3MZFons/cXgIs97XMgfsDYP3ytfbqZ3mPenSqCFgCKiMvcGuZSQusmwIPiVRraTVBO31P1AsnfBeY70z0Q4jFoAVo0QOj48VyvVCWXY+aInUm8TL5WmQ3LwdkgC6RWJSRag6XJ0SmhuPqdBgEdpQqoh2ibvpszV2D90RrxtviOrcyaSDuhu61XyvvT3MkqJUHCs+tIUfoZwJHOJLYsnU7hUS92mB/H/kzyQPO6y/Ief12rP3Pxt/hVRmby9fCkXgkmsyXsODIC7xiH9cRtrRFW/UcOCwA802i+EK/oohWhOU93PKImVl8pWjD+ukGX+yqr2/7CRQMaWC6QpZVOPg3hD2vJnjTNKvCq3ooWm+wuVjTMO1Ahzv9+STOvucOjHKKI02mP0VXBlDl1GH0FZZGuJ3BvKlKOdLTU6k2pjCatdMphkKNTMXSGykOyRRC7WUaLfNIaxhInGQfZ2J2PVGshQslgVtZL0XFgV5Gr+PoYsSSislMMj8zcsTbbqegI6XMZjkBAST5NEAyHBXpK1ExRHInC764853zsazKP9Wumad7GuQWeJRvYn/nx5dVYxIUg+dut2FC3P6dHuuR4GKtIfPbmzzGxhB2x0xLwiN3NS+ca8T3jE2lQ8Wd3ZyHSfJodyoeqpemiyUlg9IEWdwE+8Xq2EdO0aqL6arPi8hnBW3yMtJa6mrlSqJ7IJ7uyxdKJVpKmkXMgeaMA4vmwGHHgANMnaPdtt6qNqx7XyyagrWVUBAlEokho1kGW4mmK5ypWs8lHJIg4BDY8VRXuRbPTFdBJ2cwFgYw1hZ85yICVKwiBJ3E5SnCVwDLiWAdmBNoQsuUw9lFi4x64RRAFvEyLLcdPVigp56tRv0MXMr2XEQMOdrwCElKuZxY67oJ9UqXAAcwFb19OJdNDHnhrl379xPiql8X83SYfh7LpWRyEDqZdF2k4sCQTF5zDGgP6ZIrZTm+CuZIE2iJ0jJ1JtDxr9Go1kLRGH2cIv2GVGyKXcppRDjfKHqhklcueakBiI8yFuCTyzNpzIgLQYfiniMFpSwd6kadrddlmVaYTlhkulSLY8yseolYcmo0C9uY26i9kGo9jK2gOxGJNSoZ7baQMe9CFBLo3tVl3t1CHK/fWDj0i7t2D5YOPGZrUlZfR5jQm0qGKsOjI7xFXqnRE6cFZBCh2qg3omFp03Xj4KZjKLYa91Z4CfdKCNMdx8sTedkFqOw1WFsHZ8Ky240zVqwbFllFl5YDC/uAl7bsNZgbIpgfp1Q4TKrUGSUKI2bhMiLV/7yR6yEEug4gSQJJ4XiBB9dEX5eVIIQLdXcS20WWMHJ14gFtBQwmgPzJlINjC41qAdMEOtNydNClaEeI0gk5AkVKZ3CMOQCUrYaK4VSJWbgO2XztSpAJiJYehvgkJ7zsYyKT1FqydY/bcqEQfuKCEv17WkJUTUiUPc+jCTbilyH2omx4UvKKBa9S9OJEQouH2c0sfKqliXDlivzENRql3HQpn6dhorI2WiKQquGmOWly9nkjKBYDA6XnIe8NbSpz1FxXS+4gSXpVvFf8JGAhDuJB+Ilitdsr7OgJo1tDxFBPOlQr5YsFaidLkQliRxB5cRec/0JoWY1xle3uJZRXQL5MeTnkDZGOSLVSC8UqoWjVfz/8DpVEMmccWBQH1t03tiguLSQRwKIfLnLMbWHdajOQL9whj+Cjnythfivg813zmd425YIIZY3tx/T/SXrBKxHPYoUus/lUtc5EEbdntp/liv0Thoh2y6pxqbXb9QlIan3zlPgVo/D+C4Z+d3oxeATZ0862kepOeckuUd8jYmUoVFCKDp0Ls9MBNpHYjLt2dye60pFwBFszyC5hzfdBmETORTFvpxIYMuSRzkAGCHlneJd4FNilxC9MXbCbrHP0QiaViG3Z2E8GZLF5C9daNidbqYlVGufIXX/4+zuYyVoP7ZToRFSJrS30O9LZY+PAYTnQKgYPG8keLIwDqCsOKXXmajAJS+WlylPml2o3eo4QndUeTXxFIhKObAwCZqL5QX4AY8+5vMewK9lTEHrayriAULoZ2uHwvKlsRgGYKzsfBlGgkJCVofPwpQrUiVrq068RxzON9/zTF7920dUFAUos59IsDKOmYqnD5ySLsydzpUqVs0ZYGy3NjgYMqrvDZdWSKUyitNw0AxdsrZpU5miLawcOAPbfKiFK5k4LhQt3Y2PSE+rr6e3vk8SMOvdFGGYOMbGI/SV80JWKyxu1mAIWTlKnpGDkF57zrs4AsJA+8y12SkWMztXDAXt72tAWPrb667haJ5Mj0ZCnfMYCwL5AZehR5J7+fJHHP3YNlBu/gTCLqVak5IqZmp8fW/6r6kJYJlsWU1koEo7GnDxvQwXvP8smVRDjKORe6M1kcky4Ym9KgjkushlL8loclkjKJXWQBLv1qv0GsS5DPJPpnFp4cGT08l9ce831v2VNM+MIDoMZ7RZVlK5Pi5OmVZcvSE5dPYkGO4JxxEhVDL3uESdsShrddwo7M52sjMRudKdTPGjIyL44fWG0MycJ6Ky4/oqS6qIs4DI8PAKWb9uySbRsMdJIe3R3pTBCTOWlxcTJlHqBdzeWokHr6up/dK11hjUs/aBTy2yAUkXaX5i1qD5Qa7bmX+ccOMSrts45cvTVD/S5+9eAm1qf3wTIOydetXwkOOHBTwIVg2fIU2kpacSJKMCSGPImp7NIWGb4MM0plxdJsQxu/mvkQ5DDJe1sTGVzcEZ1iFxOVD1xjgurVo5pLwmysedTowNjkyeefHqkqzfMtDoY7qaPldkGVMWx1si/zmAwkLtruLB7hP0bvERSdOVqDUUYhViiogsr94iWcSdq9LhVwAAwJfCLAc5hdsKaydCZiN3b4pd1pP94GXbdtyccjR93zE6Z5tzw2JIMsB3q72Pr54lMwX9bsJvz/lC6RFq3bsa+rMPtRQbzmbEPEjPi4Dpk7gtdt/yxii8BB+ZLziXIdJ1nEQCwjtsFG3EoW1TbI47K31kSTuSd/lxcZ5ZUNUnu/cVN7hGxEJaCAdwKfqFjAnas7cnk8vUq03dk/lcWjWplnVAms4v5YV91M3uEYhkPVsIUgFeSSOiZ+xVAtTaPDE4zC0smjgmJU5l8rly/b/8Y67uwLtRY2iyz61SZnFWHIEfO6yLS5y/85gc/9snf3HKvW4ElSi2xhRtkz3On7ALAuXKVdkwlE4RzPJLmqBqwvkutZQRFtAb+Tv+99+1heeExO3fUy/LesEMZs/Y2DvVhSB+dysp0MakpeUv2UXc+xO/Mcy1GmOGuoi8vAmPAfLwMHJQZ4XHfH91J/zVeiyywOi0DB2bes2UobJ0UEQCwb2Werd/xDeOacVr5z0fND7HrjM+cPeduuLZ+5L6ff/wcKjiuSj5IBRTeQrns621euMAimfa7GSqkrNl3cutoQ2/AMi6gIxFkBbkjrMkHd7MKLj65cNf1DPRKq+C4FiroPuFSI5RMeIkuL5Lowv7MviIaISC/tVGZ11yse8OT0xxA1Igm3bKeMMgtDehYEA5h4K6RROZXC6J7cYZkhWviJ9xNkw7L6nstQBYEzxQo3FyIOzg2Fo5GBHFdHiSvFr2+dKpKd61QZdaehPuZYhT3y1xICWsmrpsX16wNXJE+kHt7+b7WM1+aLLH/S8CBVlmxBNlZFnymkVSKz1VEKltjOFGuXBZhynhbdzfwk8lkUsyPZX8FFygSTySfQ18n9fChLaGRFKoSqt3wGfY2k6kgkLJABlk66+3eP9zX3VfIse1jrcKC05k07fXNRwIpmh386nV2dkLj27NnXzyWPOaYY9juGA1YecKaGz19tr3E3V/uED6fdpW0DTd+D/j5naH79h2QMdNIfGxaDndyjRBKRBPaClKIa8jW0mAA7XJgMluOdI3nSuzDLLGc5sRaJIeCzPPiMGDxjkxxFFJtsDdNnAhztxqiBae7enhhYBqBbgQ5HIlGqzX6WjPFSqbznPbDCNb3EA9FMNG6XK52JRNJsmdMnknXUW/j4EA4nti1d38qoeYUdHKXuSySXrY3aF4FViRAGpV3U19PpUD8MGpiamq6WIjGEkwn5JaIM2sEV4RUK7TzOdD6nnV+bVZNDUQO8306+NOlCwFpCF/8qs+ov/nILfFkRwYn8rLFCqJeADwqVwRhRTTbZte7KXtVOpIjC0gII9pELoNKtnHjJkRpwe2Z18y/Hf8PK51FjlGg27CJ8c4CO5TwLxodGBgC1djTtFkDQaN2ULaIPA/xMTjaqCQ/CK6ih4ZjtXBYtuMX2zGBQT1cgTN3LjNnxqCqJS9e8mLTZWkgHLO2WhhHHlX0YFpZD9iJRRn1hXXsKC7Fc5EBY4fts6d6zUIJl/GsS8DYwEPpVIEXko1BxAjuHDb0dIqjMaL5susOuiqEIIEuQdMM3oy7rv9Lb7nBgvVGRfjU2oLrmi1W+aPhwCFkztFkZ2mdBuGzQcftGMObEctOniIQA4MiUUXa+l+0L5bFUBuPIZGxIN9+Xxa5yS+aiIc4XFcd8ZGPTr77IQ7FCB4dm2A/h2N3HsdIYy4YavUjLd8/v8ooE269aiYnK5WB3o0bNzIonsvlBL2aHRQ/8vJR9ztLmsFH4bQzRtIE0yVRYBmL1y0/NRc+IX6O/YfIluSZvFcKxcDgqTzjrjNxXK0pSMrijZD83f6crAOWz7IhFmCiB2PAxA/6KsLWOdg/k/FhfWRfrlXDKNc6ywpgl3lYjQE2Lm00MjnZjZJmkvqqYDAAdo2kDGV1Ad8lARWZQyfch3+H5bU9MA4cAQfsBToCJi0oikys8cWsmv7mLENC/xCdZq5tGGsWPz5q+eKRxUhAhP1F3//1V77x7RtuH8HPN0+4SGt1EpHYiGhXpAskwtjUJOXu2LGDLNqvAUMBFLUoZg5gFLGEwCbeTExmiJRMpXt7U1QfAEYD014IW0Y3Y7k6rMjFAQ41ofFmiBGf35o8ojpsmMzm3pCdz7LQSwjVcUI/iWsRJV+bSQOyOa8SjlUiyfFcXnpQ0mb+5B35JwwUSwdtlyuD0PXuLgfAzthBzgrA+i4FAKylLPTKunCUN17IBEuo6L9BB0PO4fpAfzcr1yZzBV4z5odBiT+szSTpljduocV1eHxpenXa+owCyLZ2bDbXnMFA+2tDNyPaf+PAwjgw85ItLJ3Fvh8ONMFVhWawEYemCAAY2RvYBuWRSGs+Z9Fr+YdkZAbzr66/+dqbbr/t3r2M+KkhWmW6iywX/9YlJRXjhIVyJRqObd6wuREKF1d0K6yANgibmJhgaTJnZyUSYpnn2HmpMq45tUjvVuYKr/kd3imfASeGrrXJsrkpvhx+ADDNRWpZCyyZUFf318yNsKlpDoeM18KJyWzRL4d1Ps653Egi02mBvWxeJs2lu5LyWYr2KbkBwFz1XcLTyjH3pkjYIV3wdqkHtheLYmXmrKWYM0ADwLyqsVC9v6+H125qelpIcV0Bp+K5TuEhs17bgdLefkMFFSVMviY54rrBMiQYJW1jzjhwdBwwAD46/h0y9WwADjRgvmGcDgmjRc3+gBGAsz5qpC8yoNyIdA8McbAgspIQnJ9K82oe4SDSwj2YyojoSKfTvd1irGbOiB9Rki6rg6Rm0YJNE1PZSDTe3dvPuUdY5jlGelmpub/CFEnnxHDchJVhX8WhOmASW0BLZRq1fE52EnH4h6GYnwNfeeTnI/8dMpNRJjtdizByHJ3MCcIFzo8r/+SPtaV6zh3bXDoAFrWYcgMTtN5yFY80+RG5YLADtutWEvFYQs9YwHKile9Np5nYxU7VbtagWKElf15j6PKpPKKy1kKk2fVVINawUqmiXRmmsVFTAp0FYS1U2uqwUhyQL93cEnOgqdUh8vhiEaCtU3XmaMAIOmkDvmb/yxcRjTfvpN/B8alGJL7v4CiaEUtU5LtX5+K3SnOdVDMylqO4/v7+ri5B4ulCvplgOf/PvFRaJ66ovFgC0IB7e4WSqakproINDklqzty6nCQeeVkQTxtxBYDdVGS4XmcSmaskjcTeJ7Od1plEvodT3PPMfOL4jazbfkS7Z82HpMVLnjIJq+jGmN0+HOTsR0EB5i2qzkxoJ+eW1LMLv587NGDZzZgzmXFSbPD61FOxUDKdxnYiy5Bcm4iKTtPYMtcWhuo6YF7aqlstRhssphlaMjSvcWBGVhovloADDk5roQYWPjjL3r8cbMa5e7qmExyWcIyNYU+mvYoAbPKfhCLIEYxIPrEP9rgneycK2VpiKjfNA/QVzVbo5N6d50csSYckcPrWeGaapUiDXQnWliC1WYokkUW+B9LWBRz+0iJWNJV/1fDZEsfl6eZYNcM1ROiBME4ThCi8cnxQNRoP1fpj1Y1JzvIrZVhRS5eiWSHsq+TgEgM87v/hKTzyJ0qzxBf6XM6Suf40GyYzya2W7jeGRBby3TwnDrSS8WGYzChAUTpUDOA3CjU5CrgqVWQnK2kNcdIScqXBudABUiMyC39qHOjr1ViWTWHSi3ITqFx0KYxdLRmZ5RHn7UTrNdZsue0tpc2JE/Ma0QZnD4oFhNjCWrFYa2GEzVTHPXV1JVjcrEfsoYjllFOd6KK5mkqBzok+nE4kGpUyg8RM1BLqKYgS2PgpKKcZe43/l1q7H/V0Gq4ygA8J7skusg3xwDvC4ZLj5BpniVWvfRxYsfdHOpIsl2laa1EW1d9qMRO53NLZD/xuNKuoo2L4SRswiPkRgX9FPOVaOcIsVqQYcjneU27EGMxjySXoi7QmvD8dy3v1A9k8h8GxIYPfAPI1c8RowgsnEOtQnvS8fQfq9f5jSt07Dh48CCyzLgmHrOTjlxw5SJfYciy7M1+HZcz4wGSxVKxt741sTDEHq5FxhwYAaXUOSKoAGSKRfT770rxVXoufQhw3Fa4qrLkgjHWwuiSF9A4xIATQkh8bOLi6QozQxQNUQn7xBkDLshtZWZPxvN/um0p5pZN6KsenvUhxfKocyhKT04RcbwMrvZ8tI6GiCiLeZP+sxbgmCvFfaksWmjV7klAXdp3g2qhnpiY0orKBaNz6NmOB5DA9JKkXIriUiXsVxO59I1453s1alK6urvumcpP0KmBNKMlZBrJSTKFdmpMm4ic+MoLpI5NTDWFFMZvNCDcjEkgMB9sVtzwrRdvVyl6kVEg2yr1xAelStEd4W/c2d4di5WytMA2FJGdUQtpcLAduN2nKdUXzlOL0J1WjZQivVsqlAjt50QyRZHx4ZIpJ0P3dct6wf0qidAVCcHxTT7wvEb3n3lGWknPsIjtcF+q0BO3iv6GkWBcOvvJr1lrbkns6kZMszK9zSHWkUA3TQAxGRBtl3pl1wRarZHs4sGJfF11JLGFcwVHZGsA5oBTjHo7K0k/XCAxcyc6FDrAVdLHiJpNJnqpRiFvCMQ+SXER5o8Ek2/aw63fkKkKQw+bcxwnicqA68ov9/GSNLs+QudKJxjAtHzABTf1Cv2H52J3ok5jcsC9ROZTK1aIlpqgKJEm4NpgIYhETghmaMzHIsFCUwod6EmnWEFfZekp0O4nmxvSc1xl+1TfvCh3Eb0oUpZk7CdB8WlNQvj7T+E1JJJOJxAE54AhrJh0ITbPHYjk/lAr1oGyJhkcvROBE5bsoen4RZDa/KHl6pC5IHXg0JSgJIFEQPKlWY4k0hLHyVcXtTFwBM6GABy7QVbFehu3siFJBVUUI059hAyypnjhpEYnKrhbKCVEgCXQppY7siIKaHIuwoYcyxfVTXBM77IQEOWmYriONlgBelYBwRLOjkxKtl90kLSlHA6Vgv1kJc5S6kBmfu+WRqGwQ55oHOzb0RtkXRXJpvjku5kA6FW5UdSWSpIBRkSg9gPW30gbW8NVIq7qeSdX5pK0rfIGoCVXMEdI7JJwekGOeXYwDi+SA64UvMu1RJWMUUJZDJBI6IKV5IdpQLxRlAVQwmDgajQh4uPIUoAV0A4QmkExa80mxA+8KOQd1ftmqmkNnQAs4qGPAgTYfPJrj4QvPyUAj43bFYpUNDWWrDfnmnWxUCenDlvxjhw5WN8nYKjxkrW0i6dUr5VokLiCB4OD8O5lTIyqdAxf3f06RzcydxOGiQkYikciVNTcBKjiUEO8QjuLIw0ko0tKFKheKGwYGmX9L5ylb9XJVd8DTIfM9RHZHHRQKVfKFWDoNsBQK5XhXUlgap5vEHOZD18AvErYSg7MC5XBHRC6HPc4cSOeTT2Wli3MIR9bUnQe8BpzRnC17KLjaiH5sLB9yRoUc5My/ZDKhGblWkFx5fXhb1G5BksOUM6toyUHiodzKWyF37jmzoGmurlQC8zJPCHMAIt2FDWyG1fDGxsfrje0aGpGDBzSdS7y+L3SkaIJ6qI7ZTgWU45/xZ32/Fkdde/3MjzqbhWfQ19cH1iKV0GLBWt5pHAZkQEte9HodMQ3i4iGcq4IZ5Sh0AbcgGY5HItzdvESeqh7cinkLJ+2oUvgA7D5MKCcvajFnEhaBkN1SDK0w0xD6TSMZmT0rI8W1SrFam5wWnZhArkQljkvQmonkNzI2Vq/WNmzYEI8jgdnuo+YObkHaulPvXNb3j/0qdEUuA9sivAmQH1g7i0pHPY+VEpexC3IiW8jSCjoAJrd8PleploY2yEm3dI9oOOZBq+KrIK2Jj/YqdDddC00SBCVhZ/EORWNdXQDR/qmGwNH99CE0J6FSDLW53DQBdBmlRfLTcCMoTXYUOQww0sPNZqZr7BTKfpPVep4xepetaz589MgkgL8sNuZwJO3OIuRW8+OhmzHFwb1BaS794S9+zj7w0vLOmO9GCJgVj3bX05V2C6ckIqArZXkeK5GYUDaRmZLtP1zmTfPM4UtaT0/YU44Ph28KQcR3DYtgmjnjwFFyoCkHjjKbhScPAJWzWdSeDKYi3cBOQhS6eOMBV24RIgGmqkLs0KUB3PIIIA/UX0E7YKdlVHjhpB1VilYA1gFpaPMxD2HX1IB9APZja4lOIPrIKlbK8clMhP0IxSAWGs8wB0ihV0IUJgU+fOcfWzM6OkpZ/f1oWV48FsWWiM7jzp0FTUn0OxwyBSL0nQANGowH8nMn2aqYnpUeglRUzwpt3ojFHLO4ZIbmyLACLYWQJx+aGz8ArATN7os0ky/6PwXMp0oqFol1pakO86DQZq687u6vXnTpnfs4oth3ASubAe4/9EvHQ2YkTWVysH6wf4BB/enpLKFuR0KJNqsZXTouksy5DKuPqoz8hjiCgTOUtO2gSJz0csBGoYJjrBoslU6n9ZFM7nJSXraM5pwruqCuZj7Qzxg/iednJgkCJ7Z0YQSs1jBu+JpAXM4blhCw3Z9PLZOwBii3VqOOlCKsaPIwqIVmsm6vbitusbTBAdETlBF+Y6xbrljFj5YDK/Z9IXZV36UG4CUDvTjtXRIC6HJFriGs8RAZrEXNBV1weBDo+nRycpIISBlyIxCcBoMDtObRMjtnlfWlV1MDbrHzzwHgGeJoCL8tVJryhY9NTGLvZFekejg2zl6OKmgRrDKN2sX2y5ExO/3BDarf280kaI8N99G6hKlusC8AiUAizxTe4iMvfohgUIqfqPDqnDx3tOETES3LX2WszPlnIvmUqHxHc4YwTrnFuJGMJ9B/yUE14OBIYBq31ULg53Q0/xyV1EK5JLN55R0TFRjNhaFcOgRXXH3dZVf/5tZd+3wWthbXDJKKkVZ6EqJEApD0J7Zs3YRVYjqXmdWdCbYIbW0WlycJmZEQDUdSSealcUKzn6vf2JQl+YtGmp3O4+/qYlaXj6g8INzJ/DCfhtIlV9etaSU5CHF1bT4Bsmn1MJP/xFFwIV+KNOo96S6ZGuCianwKGuztCdcrk5kpgXkJFZLMBRwAgOElGgKfD23hA3Dw2DzGgUVxwJcDi0p7VInACV5l1X7AS95sHB76mDoJi70a8HCrFmaQGFTmzeczwIMQB4a5Zc0rdAB1ZKhDvwC55nBU9B1FYroImlq/Ux3DC/KDfpz0IwhCyDkbIH6XRs2dEhdxOTaZbVRKUVaQhmNj7rB011pEnC8cZbosJ+3AK5jACDhCPJ2MMgzM4k4UL3JzwCqlULojhjB+4rR0R4B/g3wBehWAZQLYTAxJ5QDaT8lDNPLZMOweYe9tqo9se0VDAy1EpgraqWrdi8Phsku1BBe/Uq05Sa3dnCbOuK9GpV537h2ZrscOTOSdJJ37FSiDZnJwE9xyxSKbi23dtJFxgcJ0zlVcooit/VCakGZCWdOFIsOufd1plgDlQFnHBH3KpFpn4Zd8yJ9eSyohAIwjgpDN4+YYsN9A7qmz2zdrSiTNTh/J1T1qvofckJYrW5MyiM2KIx8/GnRI/Eib+tNg8+jkFO0uTx1L5uY6k/+689GRRV6pjOJlBoFnNce644dVeGk4MFf0LE2uR5YL7zGoSdx9+/Z99KMffeQjH8n7vW3btvPOO2/Xrl1s5wSScGzf+eefz4AxSAxycJbfm970pj179pAKGB4eHj733HMxQTPq+Y53vOPAgQOEk4lA0Mq5AIC1e9E0AfqiTvHPB+AWIlVKIidV6iG4sywiqpWwI9dC0Wyx3FRGnWzVhKI8EV30M4wDrFNiuBx4Y6dfQhPRCKOOdAJUwUSt0ZSOgGYm86WIC+ExxTFEChkioYPfDMFuStd8VcxhleQBBW5GMV63vUMjEY8Lrc3JdHSSXFFBh2Am66P2udpRWIsDItlJgwc0B5UazZXZY+xgJg8NBColLroGtCCa04AJLVWq8LCvvycSol/EshSBXallE+d81JopVMgocP4Bx/8l4t3M/wrHgECyEojlnyj+WAhgicQsM/8BdTXmdsZ2M9i0BgruwQsjXyzTrcXqgZOEreXqC+ECeUYcsUBoBaVTVakx6ywRiYqOK1HruhqduwGoazTYjZJ2Vw1YCtLsNbv1feU7AoCRVygJtMWsKRzrmzNW+6PhgHxlK+V0Q2CUNkD34osv/qM/+qM77rjj8ssvJ+QZz3gGiiyE9fb28t7//u///t13380A5+233/7mN7+ZkwZQedGAicb1xhtv/NKXvvSJT3zi3/7t30iieieeQDIqEBKieI+nrS7CmBoDjRyd4p+7gCFWRC6SnPNUenu7qRpkz7B+RqNUukTs8TcyPhGPhU5/wEkT0/nxPCt5Z1Ahq6ZMEaLRAsODVC0S2j88ke5BzqcZPkSkplPxRDw6fGAkGg8D5Sqwydlni9wjx2dErPgIBHWdDnSw4L39g5/43x9ewxE+4vz0SB4xgrsg/9Ki/kJPUC31c149s5BKwN6GDYNa2MDAAC2CBgyR1VoVo0drbkvoJ3/9aZ7lmjAKgNmXYQlyshxODE9mAWMc9XG0UbMGU5w1frP/IJsAUxk4GQ5FNm/sTyai9Wo5V/ESLp68XRQzU3FN7V+ZdZVMdBVz08du316p1tmSs0kSdgl2auF4IpJLJ20acK56g4ODDNvLIQiy7k74nMBPRSLRafkgnP0B/UvesUB9lfA5jsTAhQYSUZRaVrJOZsL1Wl+PmCKE5hobYzHKIaRv7qIfxzKr+DjNzdsKwLt9YySmOTeOgMyhdRSAxSINWwyH7d04Og60S/YdCVVqPUZ/BYl//OMfk4Qt+xFKV155JVraL37xiyc+8YkE7t+//6yzzuIgd/xDQ0Nc0XQR4r/61a9uuumm3bt3b9++/cQTT/ybv/mbj33sY3/1V3/FI+LgwD/16NQJ/DpgrIHtvqpeREXmmCdnKTQt4q0F08AC2T2qVGuEapWhvm4vGpvKF5mMI44RU/aC6O5CpOYL5a5UnL0FZfVvxMvl2TvBkylpLmIyymYWzNgkTHQaydSFNy8ORp0UYWB6pnQXCQi6/d779oxN/frWO571hIf5gCuRqBZSRzgrSSVr9TVz9ZHM3ToNkdj0qNg9CtVcCdABfp2hNpNsSXzKT/TyFsaSMXdaQa7wIVuimxEp1SN5YVyLk7rIiqDZlZJuFE9YzktXpyvpJZm6L5PT6fVI2pYGbi2XcsRl2XXFC3V3JdOpJChKkwkl8icNQvEcz6s8Y7AevrJcWHlLi7l5yFITiqBjEGjarkSymNekRFXHiYnNWggdziyBh3P0UH5Bdw302GvEadLE5Z1JJ+JTHMlQlG1eaGdycBO0m3mu7//SzXIiBQCmE+cjr3DZnHFg8RxQkbj49EeTEo0WbZV+JUqbvt9gJ+6+++7jLd+yZQuZs7B169atn/nMZzAyn3322X/+539O4ObNm9GfLrvssoc85CFow8A2ga95zWuwSLNpFIIK8ybZKgAj6LFjK50skFXPMlwRl1CCrGz9SPG7X3MMGDpEFvvw0KRKgjAFFqvsn1XdsW1zKJo8MCGzfmrs4gQOR+MIbranYr446gqCAZnA9rTjk1kS9vXJLpb80glOeq0zBCwg40Sw5j+LIA1qvbo3AjVxz/jUZLl60533yOIdXJNO2YiiKX/cA2zmiu8uhosoPkRTc941jQgr+nu7Xd5yVgS3auFwOSzppdUqLnTAHv35pcCNiUl0SwDY4wA+EavO+SJV/L5UdYAnfs7PJVW+WOE0+95ujwFdIKwwLYPjOHnNpFCt3JymZO50Hk26r7enr7uHWeWcjOQSiapNQ0CgvB6upOlCgRzppsiSLyJxHrBDR7whznKgM6aWEnmFXMmkkpfJL1cocVn7F8Fx8pDuF05ZgBWFKdVdjM5rmERxUO2U3oGeNEaWsaxTl2VLsqadWyKvd4fJChbQd9TevD8LSxtivfPG6r94Dsz6ZhefzaJSMrKLKNEXGimmOIq2dMEFFzzwgQ887bTTyJU4T37yk7EwX3HFFX/3d3936aWXPuUpTwFTUYWBWzb3Jw52IZ2uRVaAN08ZA+aDQcrzlCuZc0XPvu222xZF6cITOT1Ri56jRiAYqTUdDl80NvP2kQCSnXTMFjzkPZtvbN/SCwIMj0wQ0Ule9uPgXJ2ajBXLDlveZN6fNTM+McVptdgVeMKPU+2AxiJH2JJls50dS5pF8t+Vxf8Ah+SZE/RYJ2vx1FShQnqJ5WJKVi6yH+ICJcnhHfXF8ABMqMGDiAwraHMEicTc2Q4nBnaol6solLoFstj2xwHUWiOSyTMnGtfkjtIgqIZtuOkgPRTmqHoOAyYmb1x3V4qnwW5rIoSVrXiaznmFqbk8R1mFe7u7ZPFPOJLJFpqPxP7v6COWxGT8FaRNp+g3+V0ABVrizx13nCv3ZwwYzcwV2OkoyWumzKWmdHoA9e4uKU8CZUMsYQDVQQMe6uulpmNTGW7dMDBPzPkc0P4iAIzj7WWYoj2vrDF8fXFArZUrUGd0Xx2R1dkNyBp0X97yt771rddff/1FF12kmMpT8Bj60GiBZJSnF77whaAsFmnEOvB8ONKxxPJofHycseEPf/jDKGHcLtvkLASy9pHB/hlR7qQi2ipOJCPSD3nJd+ykpoT6TsRxJi86bn8quWFItrccm8oSEdWIqUAoZMluJtN4e0eKmzcywihHJ7FaCdHJvkkBzvWkOHpXDgAgJkVwBXXwzNCDgiXgNBMg5dPRT7lzhev1AipbquvguNeTxu4qD4FmpwEjwZ19VLKe4/zckPu6o0MsCgCPgymsoNXotBqV1xkAmhiJJsrckrj52fh6ZJ1p96AN9B1wZpJGJFquFGSdNJuWaNHCEFjUvOO/ECbPiEbUaKKbYx4xJjMCO52dYkoWDyX6bD64FH5lWD1H7dIp5oCzDylrmeRcDclWOC/9AuEXFmO3DKkhG3Gk2STcQbJ0HPHh6FmWsIC7mbczmZPJ73I0gtaGqLxOAsBeTJcBkxnaMGWTIfWmeYdooP2Z8alMw+vVPgFm9gjmaHOu74KMkhmOiQQ75VYqDekC++1jDDIOLJIDK/YG0alXyzBCgTcboOX2LW95y7e+9a3vf//7YC1CR994NFqUYxVGTJAGidF9AW/GfUFi6g3KgqyE4wBmHgHe2mNFOSbPu+66C3FPIJ5F8mnhycBXJO98AEaoUi8fgDXbWYJU5DJuKoO8DQ309zIEzN5NBfaRcnI+Gour1nbn7tw//8snLv/5LaQGV0iGBow0pR+jMppxRyylrJ/mEeFynSnIlTJzq2W6qzM4I2JGs9npeo3NK/YfLEWbUphkUi1HSUsal5vc+6+TRJMwCBEMoavEzcBAP2Gk7e3tgTOYOghUJwC89E6okAKdBowngK6RkRF4ywsGCOWyTI0SLdB1MPgvdPuu+XFAHJNusOV3pbrhBDoq09zY26sZb+b//GpwIiTw2pNOpWnHUKS5+MovpaUwp1KHIimxb9NX8p9ohoEG3BJf24Frk8oZKhyiOgMLrx85kIofQxFMCGCOdVfCAa9/YV2fADAz6Yf6+/g/xniBZEXObore/Cq1FrRu/CpPQF80YCqNqPGt0OuGA1bRdnDgUF9vO8qZlycvNKZIpLCqqoiY97znPZ///OcxMh9//PGqHyMiGScGUAEVBWBmZpGTyu4nPOEJ1113HUuSeErg5z73OYaNN23aBLyBx3wqKtZJjspFKfRe9eOZR8vSBwAo7ufGgGdnL/Ju1pyaFqGqMZ3IYxssBPfg4ACKfDTG5r3hKVmiqVJTrpf/7OrrbvrtJf/34yILS5yyOcHGHY1GX5+/2ieViDNRlu4Lim+APbNpOfQdSIWWNjoxiem7Ho4OHxxtIVG8lC6qIpkeKl+NLIqX/IkDgCGspyfN20ZaOauYRVMsq9XHBC4dADvamvnqfzVEt4SNjY3xgvHK0RD0zBCmPPS1TUnv0+WolwlLoCEHO7JKiLeIQHZBJW25JKaFZlQataWAFu8U7SiN0iejJaEIGrB7KPZnkpDcMVSyyRekJ8pbKhGaR1q4yGKCBk+lS6f3R3J1PSD9aojOOyAALKubZOkxzmXGxc+SgZLeHtmikt0oCdIWaTagS7C+L3xHMABhgsPD++NMWOubKVb7o+bAigEwAMkwLRINxZRavPKVr/zsZz97ySWXHHfccffeey86LuGum1n70Ic+BO7ecsstP/jBD4jGcuGnPvWpyPTHPOYxD3/4w1/1qlexQuk73/kO0V73utcBxnwbypamEPGHmQnUj+eomXbYDBDCCGx+zKYRoY6fnYhUTOtUJSeo2fFAZmFpNqIRi49oIc5/k+P/ZLVMpliuhyK9XbKiuTtUSYXq2UKp4MkxSizbQWoPj4yd+tDfu3V4+ie/PohyDBM5+J1JW91OgJNlMsqYp1sI7DJH7KLliOXarV+RQkW+uglEzgLJA1ES3WGCCXKbnGZjxEIlemBiDOul0CdghPTBqOy8LrJ74HKVSgikoEry8/HMi0FbTsaha+mEh+5A+hSTgBhF43AhgQF5A8nYz0wzCZGhhKujcBjCD4+QL/T7fncrIfJzoeSDg0tucQ2188NZAaaE8TSTZ3MSTqmSfgYHIpXl0CqXs/wjlttCRHVi5gmzsykHW1XoNNXSyRBViIYblUgyX08SXeYRuxKVBkeHEik5EmGiwFFWkb5kbIiTI8OVfEm3/oDBgoPCKIkoO2NUyxWaT7Zdlcq6XBtsHOKc7CIiZJCh3pO0gd4q97xoAuK4JiV4I7JmTSbB1WE4NBOXbgQDl6x7IhTH+YSuNeT1A30ptCvJ1useHRKpBaddkp2LSiHNcl3KQ100zu+MdqikqyvMvXizpm3ABhhSqEdZpc3oeVq+ULHnuw5UC8tXVz2Mms7gwIyYW3566ddTKNoqGu0Xv/hFgPPRj340RmMwmLnNhKABoyeBys961rPOOOOMv/iLv3jZy172la98hVSMdPII3EWpZZHSK17xij/7sz97wxvewCPNFk+AccvZVxUIYoJr3Jtgp6VEmBk1fKOhRqwc4kg5B851dkKIcO56yYlDvmaVsuFaySuNefUsshHQ2j06VQnFNw320UI7ksXBSHn3/knCJ9gP0QnGvQcO/nq4NJw89ls/vz3DtlllLzddSIbqOxkzZsiq7vV3pzlLaWR8FEmP/GYjRBHEAu78hThQTw4eRiCLuHU/B3Il13UAHypj+VB4U7aSLHm1HAfLcuYteC5nMjHoHEa5FgEtrw9raaIO8MhEugaklYI4LddlNTzllZm2Ha5u6fO6mRpG23WRKJIp18aoC2fe1aosYoZI0SzIMYRlOCpCzjnUNmGR++FhAnZDZnzX8fNTVHb/fVR2xwnXC0wjl+QOgP3aCYJSBBb5A5nidCPywDNPrpdyu4fHY8k4HR+yZcswYZKs3oJ2VgEVvFoe2AQShw9MJmPexr5wyvO2DHYXooN3jspBdWJtkAbXnpPbCsXnJUSBgt5osZap1o7tSWxiX+3qZLwnNl5xB9uF2CiULT1khXHJS9GylVI1XM1vYmIirGXoVba3rEAwdYyluqLJVGa6QucKJJaKh6L0K+UMYZ674WTHLR5SJhSFefnYuwnU5jzjRKPSxUlHU7xp0c2buh2f4WOq5vU2wt1sAIJdmqpv2txPz3ByQoY46pGUbH3JLD83eAELA4g9pCd4iXjauQ7itabyT+bhuV6y68LtL9BUic2xyvZklTdp32Q2lqAXTV3lGzBnHFgcB6QbviKOSaRsa4UhGq0UPZhRXp09BKxihSMcFRnCjj322AsvvJB1R/gDjRY/yjHTVUgCPOstaI0HR1oQd9nmW2mhLVe+Xdmayqkg/Pc/UOYuqVaHROOxSDx0TT7zkKz3TYJATtsQ26NTR3KiAYeYOpvmqLiucGGqOF2Spacp0Et0StnTuBhOJwa27jowedMBb2cvUjuRjgONzjHZJipbNnEEnhBEmC840XxEwqvqSVkia3B4xMYZZmYOVEFLlQIaiXqoNp7LgEoIZY3LIyGQuJIHIl5ScyGVNIBowOCXgqMcQ8sGmbAiztEQqM9u2jbqLY1e96JF1lNKEqgTaedEPf+a+qiQTTFkzEOpgXOkgBaocEUL2ThCiCCHT4hO6oUpHid3jjy5AeLZ0zPiZScFh9I99E9S6LK5vBz1KEs75Sdqn6aQzobUgkAJKRUFzVJRp1DGQqVQnP6VZOuYK6XIBC/+OZlMKJVy1g4WK9HJSMdCIHciVi9xWHXJq7CUDIqEYnpfCdkg01WAbk2MVH6lKBC2MEYbwZDCe8Ju1jxhaRmmEUpy7wsFkUCYAyGBIQK//vz/DXagjJWpDcDudsEiH/f6ScJIiJ4hhxNW2WCE+2JJtv/gV23UUcfJRx0eib1OHLV1bwL/lauFRph1jalGtcvZqCpOGPFF+221Tthi1VxqDqzYNwX6Uhe0VfQLxleAUiYqY3Z2FjjZThKEBkqJE6AvKjK466YviVjQmDrBB/QlE7Vmk3bl0FfoEjEpklgcROonylV4DXbJ+TriBVuoBipMqQaUyaoYgTB3RDxmyqnJLGJ9sE80YKanUmvm7yAWkfpEPpit5quhdDJy9snHZMb2//JntzFXGbWStbZIdpLwgw9Mii6L3HVOxap/AyFkcwhHQiKSJs8RCpz6EGowHOBkvQDG/Th5yl8TwDQmWelG0L09PYorwoeQmD1gDjObJFETcIg8z6GGVmPsWwVrgG2pF5gOp2RLRaji6iAWsGxaa1HiRHl13JbsqFDThUJsy3VwbIIIGzcM9fV0szkz5/845NRG83nSpEiTs4aN/TSmMegzmRn6udIc+nJqlQM5DOoSwTcV45PqT0dCYSZB0+bUmsMiMxmo9YHaYbS8EvmSvDB8DowwkoXCuL5DRCacpxSKP2AXfokq6OtKkvtZzq+F/guzjErm7Wq/lnhBGn0bCaFHq4PimgufG3HIPfhxO/+nkVvjaMgau+oYMPY2HcbSeQOz2mKNVdiqsywc4MNZMSenxDi1Vd9ppAwSCr+usAShERY6oMvbz7tOZJ4iL3D4ic9T1ZuRTTwiOSFE1lQrVTFgj6IhyQGwo6IFW5C2oQiKBfLUaUxAS1xgGKOigEiSybLRRMSfuMQ8NdIzsF2vVKeyGcQfWaOyHcyWC/XI1g195zz8+I3dsZuuu/bW2/dyKvKG/h6slCoN4QZqNdsoitjWdlbxOSN7Hdg6Audc6Aaw+TRj0vwOjIwF5M+8LmTV4rgTjQ/nLm4nCQmj6Cn6DdXaQG+f63X4Ubq7ZC+OaT2ZQB7MZOw6GH7WDs2BxrLArcz6EnDVZy3oC4AS6FAPQsUeK30ZaQOc0NPMHOQPe6OjY+jhm4c4s1G6gBy6TKIIA7tc3fH1pKcYVw+X1g2K0zuETJbzEs7qcxqX3p6wJWCNj5kEgZt+ah7mWK0UbqS7UwT19/RitZSJ0K4mmjvhRM9OCwPjUZlfPeOaoMurTok4HilpfrE+xjZTtKZthgm8i43By+ammZAgb4V7BH+UL2ROAN9Xd3cKP+NB2mWjgRwnJDZJDpl366P7iSNZdLjT/hYAjKMqBsAd3p6rhfymbFp2evi8VQlmSip+1iDpLYIg6KRDlC7bAFxFLlQqvPd8Cdob5akKJh7p90CIQrXKlGWvkxYokgrRpZ0Av48s4OFLMEEBFofINCzBDdDjjv2lX/12GGW/UGNxbQxNDxWJRUBgSTe2S3dYOrlxrjt+wYmINzxVzjfix24ceMSJ3tknbR/Zd98NN94ynS9vGOzRET4IYPpWNBrPywbDzvnli99X0TR81tUdnCRTk5C4EcrmN832iS6OvivuqoTMpESEt2Tvv1Q0K2RMTE2h9rEI2EdEVwXamofa0/JZJJn5II6vpQDId2uPyUvMwk3HrYQEju4MNnMXuVFVehxeNfdzrFCJMOri2PgkKu3mwf7+7jj7IGfZv1MKplmalgmHiD4JAqWyYeQE5zxGIgPo8WwO05OmOXLTgtw8lyhMd3L/yGdG/XVDDPlsDh7q9qAcek8N9ZVW8n1OMVEcUA6mQJOtVq2Jr7zPsMt/o1o53YwgNT20czUTCmUJMmUw3EOh/AiTYXPxuBmBUrrox3xBBWeZ4C11WbpOj9Jz6CI0E9edPHyETn+C2NEGUukElw7P806vq9G/fBwIJMDyFaklgZp4eI/Z04o3W1U9BVfkMuEaQRVcQhAcwDCKLx8AHrQBIiPLEBM80m4pGTJ+jLRSUbXcVWopD3kFhQhNf1tBgVr/gxXx50QbQ23ARdbz/vuiSz7z5f/mcPhGnBPjE2MZRnhRQEssk+npFk2O1cDkpnCFaAYr7tnPVJ7oMZsH+j3vsWefmgxX792zm9mrm/p6ZXDRLRHS9Sx0VyhlRlg0ZIRV3ay2Rx6rczN6JnMo5lH2mU4n4wwtH5yUZ021TeM1r6JV8sgpWU7Qu7IkQLR8oCUzBS+G+vq1BEqnRpx0pQDMrRsaFyN807XS5YIlkptqJKRDP30U8mg6scFyy4/HGo3JYP69WPWFwDDbOLvH3sjYRCRU2zLU298jjJrMTYOvEkMuMjBPu7hSneLoBqSBIGrBoiWOFKTU3m5fAyYaGMZV6PIxEx+vszAZfXVaDkIucSwVnSGioXPTdry0SolUgK3LXPJsrhALR7qSjBSLCwCYvLjltYddvAOOMBfjCC+SEQgr9GSnOUI7wsdCji4U4mXGvhgVZNVTg6lYuggql5MFS3xtM4VI+xwOYt0jomqcmTRryoe0oT6cmIqooS3o8S+4LdYUP6wyS8MBPv+VcQw4UTCgwtuMhyVJOMAVcMLiCsTiZ0KW7pekyjGwSggOeCYaETAGkhzH90B4gLs6GLwyFRNBJU4JU6z1QYapRg46kKo8ZaoSMZmSc8Md9921b+zu/eONKNOJ2W2pt8DHXq+xjqgvKbAw0NtDhqqrISXh1233HaiG49s3DSKwH3X2MSft2FwrV2BIXw9HEQquIDuTzJzCYFCuinB1JB3JRQkemyyy31ZPKtbfnUr19OzZP4bUdhOmtXYQxW9GBIF+0OWrg1qM1FFEMnOz69UKh8ArACstGELhQL7UXArs3gGX3fwX0oVQAalDpObFyl4CLjmFU0rlBIOql3Czl4MSxATNDRWXYUzUU2zLEVFkMa4ewATdqG8e6B7qkSOFJrMczSBO2sM5yGiySzBYMsFSXciiyPakZEMwWAIU0kMimkzN0mTuSmlUhSuZlDmFgnVjjXoiHqNViNaTSkM3r7QUQSSubkdRbmUJWSTUlZBzisjB8UMiKADTKPLCtOxeKoXORJrLtLn3xHXrjGFFMs7MAKFQCnKlSzHOEUK7APZsWoOCJxGEFNePkq6Um3bX0uguEeEu99Zo7sEauyBzqBEj9PAHD6Jm4b2hNcYSq84ScGD+p7oEmR5JFoqjMro5+0VW6zGvO6KH/jgddvqeIgicIwQHPBNCHD4DFUx8FYSTVhMqYB8JGUsdB4R12o8KL8ScE8dOIPtVoEQBYPBKVA9v/6g3WawkeobuOcBKIjmDAfV4ZLQsPYxYhIngtJDOjgFKiUAu/PaPjIcjsY29KZbjcJDcKTu3JGPoT9Fuxsid0kcpceYvsbaUM/hmtDS/uqCGK9xJTw0Ty6m8DDoGOTFdbETi6WS0Nx1PpLpG3TEPotX6GTj5LX6xYgZZkZ2CmGjFLi63RWy/9UaXk/tEJgcku0oxGosk/JRpQd54XDQlD8jj55KFmN4cBi/5VdzQbDkkmFryoiWZwOwruxqXK6o/T9nDBI6zpyLEMOEKxA03aoPd4KhUdoq9IrUcmS0OgcIa+QcJDBOwJMllQi3CIekSQUqcouq+DkRMIrgMhD0om3Q6COTHUtFipc5i7DiTv11Iki08GGgoy6i8JqSTQgj+crXEdEQ/pgAwYeIUgOWFUaehM1dS82t1DjIdt5qhEofsSihtct6wmF80DeuylHzAncjE4ctiHhgUMnme2yYZjhh30aS+VwrQnBwDJIM167RzT/OrgJLO0Fqu7pptx9VWMf1+VoAqxVEK1hda5ja42Q1KCoJAVTFED4qdiiGNqREIccJCJlG3hhO4ApVpKVKEpxNL2ekcE8WY6uN2opIhRoCKPUKQVeFohJ7HwYMjpLv1zvvi6QGWtVx70x2ghcz6ZcQxk+XR9m1buJLbFtndK8TgJWmJw+Qf7PP5zORJx3T30SVveC97/rPrhanydHbnjq0URBralXNeOYEHOGCGrViheUAoa1rqzPWSWPI3z3HMHrE4LIjDf5gofMy2zSMTk+OZnIsocpankj3yuVUCOWHERQFYc+UAHxKMTYx3M3wQEWTnFpjCxrtzx3aYcWB4hECXVB6ph7RNwqS4AkPYrKoGQRthRqahg6GLUZbSypC5XDNsU8URv9mir+xCGyBIhenNMJVM+jQQGg65I6Q4SooNO6ul3Mk7vDQj3LEYa39HJyUyjeKWHKtN1mPcGgUZ8zsnFkJPJjdVyGa3bRAEZasTcAqDTVaGlTm0Suj0uaHDqnQ7qg1e54OjI6FavS/dJc+xZPT0JiIxpjuQQBnFQdFkTjaVRp35/JuGNgglYU7dkc1ZyVPffLqhfAXMlqCLUaqI5g0ZwpPmnjPciXM46nv5sjh/2hlOKZsk7LRVa9Q3Dg4RQRqxjo2ETSkZCKnxCbFPB90EzhzjKbvL0cuV/FwTs7pAomHHlr0/mANYdwumJU/50WERO7pMTJJr57ugHu7UL9e4cC/PHMcye+4huGgaxhGYKdnsoHR+na0GK8QBvmhzS8YB5BHfq/5UY0GAivYhp+sQLF5kFYFlp41wu3d4hAW34WhiKi8LhgBq4mU4rJ59l9hz2G1qgSykd5JFeyt7Q3FvPMOuScUNfRyLJ7oh61v7u8Ive/6z2BN7aLAPge4UbKbUeolkrFEIs/aGgvi5PykdlTisauW8qmMIJSzDomMv1JdODvR2h8LxqTz7c+CcWVKTIP35CbHOSe7yuFmQaMBwgMcs4MHUyRiwi+eDq/aTdOocuYiS6ucld/hdcfwPx+Kgl8ADFw5GBKt+fO29V//s6nhu+pitW04/68yeTQMbdnb39iThHtOYXeQaQEtMbM9gQjwaKjmIwJ8pg3yN7mSMmaywLpVMZBi7kCXbWiw1YBhYiBFMwjaD7ZdJ2II67BYS7oqLbZ980OCrjRp9Aw5y8KtMw6IBs+6MxDhODwRZK7VoOJSUJdDS7olYnIXZOn2ap1JPl1iKcFttoWD7yd1L4mJIZtoZVXGvkCyhOGmCOU6KCkIlslRHXjyAE6rU9iBJWwh3WUhTaicYy4RmqmWxDQi3Vc4f4ChhDplm9bTrQFAKHobGBczpCvgHVmvSNXLVrqrYkOp1WoG60t2HLaoQ0yKzmmONVNqqsXwcMABecl774k92U2LEl3kvTlA5YOVzleIi4Si9aVXc775vN2K6u7c/k5/aN+EdPyBCTU5mbFT7ersACVIIVHT3TIznRya9HZu8vQcnQ+XpnVt3JP2ntd5Y+PwnP2A04/W6yT4R9m1wK4aR+AgIJrU2uh1UutKdyKCQuU4EsDh5NJFFe4wO9vVsHOxDgh8cn3RPZMwQ5OClYTvkFn3LrWimhgCleyqRBTYYssXkS3+htnWjjgFTCAPhYdQIIujEFsrTn6OO4OZ/RwmTgciEoQXkO7BAzF/ccNsvb/jt1khs/57hq667gd2qtp10/Nve+DK2uHIjqOw4JagJTN43wXnSpYcel2AJE61CPjAQgN3UJ/t1gkvMikJ1zsg0LKqjRUOhAIw4coGasIemy1bQ3fGunqg8oxHRRyulQqHcqHeBucJtCXXDDZpYpkW7kV02+WIimzxnDDidqpaKTJ8mPreSypXEZbpYoPcgKumhHKJfejPNwZqAQYeKOyssRDNJITKSnXedPiin4uTALi2OBB4GoO/p7HSd/6gZUSx9SLjAUEY4LpWDbPR8+Fkq07kR/siASsih06zC18KNAjA6Mcxn/zp6ZfRRaA7to2iXaC3U0+qwQhyY+fZWiIA1W6wbJXIbcSDtfGHnVxYthBVVyDXmII2MjKH/9vYPMFl530GZH8svhwbs1Tf099M8SFBC2M2fbRrGszL8uX90KuGVj9nUR3Y8QpqiBKcwWffKJo0S4sJJmIhGGfbLTjc3qXCPkClOgmtEF9R6kZmxAHAe0gd6u7BCV+vhUbYxxDlp5CfT4WCZ4DXjEM3cC66IQySLXzXgwX4XJJJfimfeOv2AFkEv4S7nQ7yQYtbF9MtpeuxLySyqiUyid8Mzn/vcxzz+CQ847azhscmb77wXbKYsMR7yL8KcLIn5v9//2YUXfffGu8cACIL5DY9O1erhzUMDGBXgD3tx0E2azOUFQqmRFC5WV+6EGhkGFYqn8oQ0UnFfbyahTiLLFtzGzj47SQwW+czFAysyOSwZdXCXfHAD3SkGIZiERb6SNQ7KnH04M52jQ5Z2s6Cd3Zd8grxE4eZ1wpHC9Z9cWnfjONrsMTSDqYEwVPpAQhVJSJmjIyanLUnXh1A/Hy1Mr+41oxTsq1on7ABylofToeMyEiQvmMwS97uTSr60NDMS+MfMJJe3K7tjL37dW+gvM42hXqcVAGDpwYTDdKBbnpvXOLBIDpgGvEjGHS4ZWpDKVr5YOsh8qyIDEWIiKd2gKJs+cBAesirkDQ/X0AJ7+7dt2bZj8sCe4QMj9VN7yBlkQqYOcX6f07eQan39/aG945O5UtFLDI+Od4dKxw4mEXeClqBDnX2HmbYcmyw2El1oX2o99TBSI2eZh4z2JWgiJAjhTuYjhYEVSS/OCWyXWRUA08Phe7uSvd0R5uNMujFpVF70HuJIHi4rN7sL6siPKbKSWusuGWKTDEeLYoatxKNdqbgAkqANTODgnW6JAhQpYCtdjgQJb3VsiCgqsDMrMK1suO7dt/dAuN54ylPOTDF8mfDuGh3eNT4+UvSGkm4Gmez3zMixDBxfdc0NYxOTp+/ccNYJjydzCB0eGSezzZs2wCIcC3NpGTbD4qymgHQh0T2FYjykmoKBrHZLpkjFj3p2p9P7x0dzWNe9tMNQ6ubIF5uHYD/0cp3McKRVnVOGKJSEfT0ye1tOCHbZSiE0sCsMOwEAzCFehIGGZCYAyUO5kRcGj5o9feAM4on2KTk55xPevKVUivXRdTpfJC7DydBPCunR8BNDDWTQfJJLf78sKwCANQeydq+vLBcMy+6hmMo96YY4+qNgrvNQAFPEcTO0yV2nOuFEKyPrmGqkIRg34VtGA6aazCGlemujvp3aTmuCbhGa5trBgQCAJXMnA30bJeqcW8lAhHvu3YWo3rlz50knnQQu3btnL1IbYZ3JTKJjDA30IMWdGskZuv0Mvk1kZPU0U6BTAPCmPnJFJoqrFhL1AsKb8SmSA8w8Qoyw3TGDVjm314S/ykaQzAlLTTj/6tSsbA5hHSb5QC9aUyxfLBUrSBwp7bCJHZT49BDP6WAHJim20ZuWDTJFO2o61qAhvOhntIQ1n80Sfpx2EJZxUbbBlj0mvfExrLWlrm5mZ3Mur4c2hy4CtOzdlwFqxGEGZ0i1JmzMVxrhrp579uzD79DGATCrpYcGoYf4mKCxSE9lc5LQYaFAntwosEhdoZA9yBC7KLLcu56AaMA8gn7NVuIjtlsqQzh32SzQXpfzjx0BrOpmMhpdrhbtST5Ass3lMkAdZ1/hb1IguSL3uYLNeHhhJLJDSnl2aNe0gQRPXSo4wBnZ9Lcc/jafuUdy0yyyr09UPQCYksTwwLsa47UKF0tyIBaOXhaeXMUbnZQeHT85gIKdkeWhzIeU/2vASd2aXRs5pkPYLgAsE8VFadHRE+2drIHqWhVWigMGwG3hPN9vU1w6xHLyKSiJ75Y5t5zovWvXLj7sE0444eSTRS5zSxyaZHxklPWjfb2Ie6SAaLmsREJajk1msLUOHxj1KtM7Nw8wDxPBKimYTctMYY+lwCITC+UKyqITFqIOam+9BR0kTdPNBAvNEiq0ohTWqo2ervhgl5fsSoNSuakMWKepMEKqCzkFS9I1Q/wH/HM4wXxavEz5Rmi5AOkaQFhzkxAWmB0iKUmII1q1A13BvHLBy+eQ7vv3jaSTXafQX2FWEWKRjbIHBpm7q8KxyERfd3oDqMGcNdZUJ9N9ew+M7hv31zuNTU7RIRjo7yMrfn09vaB6jl2vqbmACD/hgdSGrHVw1ymy0XiM7TOhCssEV+yQICJLgTUiV3HNyigckck0q3u9EDtQ8pCWSnIGg9u0fFrSzXLMzOKl0DlQrQ/mA/DMUy1m5v5QPgeYVIkfm5ISgzdEMF5rKgRLXcW5gWw6RpSoQwPBEx5yigU50O50HfH87Oe//u4l3x9DvadeBMYlsbPKSrt1upvTw4EP7kxtaTt5AeiCNBpmgu70Vl4l9K+FD2a5Wakiqym45pTurKwibatOfDPQCxA4vUlM0dgRuYuGa0jbnJfczWmxjdC2TQPHDbJaaHz/wREiIx+nMuOhRpkDdJnBxWQoUDTFTFr28We0kplEU7JT2Ib+Xmk8MSJiP+RgQfC3qpN7WaXapJGS4hxvA9jITFznQBA3h0YKUruxhismsRcmEQvFaVa6pmNysGA6FkH+ThQb+bIUyB/i1uXPDBzRBsSJR2bBEp8I6KDFWGwaUg9Oxmq1TSnO3hP7MHXhJB+iA2XxRontrHV2NTOWJFuXU8t/mftTLqHzc8Zhil2y6XzsOTBer5U39HaRIeRRYjoRYaPmkfEJyE6mEgVUZchgLfWBSqZcGK+Wd01M37V/kkAiT+c4KSvanejCL2mTrK+tseuELL0igENwsTS6KghBMQFpVPhSvhqJJOKchQTquwbuCnNQVK3OgVUOyB0nnXXeVUMrQuRyGRMuI7uyOwort+QaqZeqISzk5Cxl4pzmzHYpkXA1yh5fOHRd+ccjmXsFnTE5RVI2ISHM9ckkrdg4wFehR1YQMdHM7YGNXdgfBJH8BWHFKMLDaq3I7Ht2EpEGECeZy5TvEDkTV9Cll/GRWjFflcXWpGq2iKzX4qneMmzww6t+/a0f/OS23fuJAyQ7ohWAfcK1AHmgz9x/vWsJI9bs+H6yFftHBWGOsE94B2tgrJzJzCIxRhZ0NaTMRmiEKnIWpTnjwNFywF6j38VBX2AgKQ71axEneOUDFq1A1qdmKxyFkEykegpInwiSNcZBTvGoSOFYtRCJpYYbqSt3TRa82MnHegMN72EnDjKP9/q9cpBtqFaOVgvsFEGOeXdM0jGbN7BFb7lQHi5ggy2zccdAXyJU8WRusZwsm/YiPRwbKzhXrnazXAYDad3bsHVntpYocCJ8DQhnvjDTcMSqHZJdOpzk5rBd1sySUa0SkoIQxzLkWihP12vF7UNyuO+OoV5GH2/Yk2n0bADYBQSYFOyVHeKLdlqVeToyBydRrSWK05zfAGaOMsLteZd+74reSv0V5z62220BhgZdkOWp3qawtylVn6hGd2Ulu3C9xLZdMSeO3S5e4tMtJ+MJRo/JP1GthDOed+Ou/Rifj90kNY1UPDh07JbBejgyPJ6D/8wJi2GVpu6TCMhSoyt8EGG58/RvX35dFwZ5z7t3175QtOfEYzawowc8GuyPJJLhu/fs45D6MuUjY4v1LgHZMj2dMtBXa/SHvakD+UopOrRpgGYFmbDFHzvU1V3OT+0Xu3eaBgUDOSKQ9TnhCgfwct6jTNuR7sJ0uVxlNzEMEzrI3DvUW4p2TxVk9TY/ejb0amA4W1/kpsc2bZQpyqxcoi6yPQhvENt5Q3msEU71jbrTH4T/WCR4GIo3pKdFXI48VIiT1JzqQYaswKIPx0NOIITtUyUAMtOTqvQIALNXNilYG82hwjGOha5FkzVya3gnd3tbU9X7JkoH6AmSGXtrA/31RjIWZjuzVIhXWFD+1pFCbej4u8dYwCV9HV3n1s1yZ9e/hBbn5EMQ6pyajUEbqmAgP9e4POO//lz0VXGhetX81BTdL6iVujfKTCbIlHJYpHqTYV4udjAtczBoldabNaqyKsg3IjqNA3xk5u6XAyLqiEC/1/3wyq27ipbk3zaFDg/FYszXi8RhhYqTdO4W5Q9rHUHIrCg6auie0XIh1NXb1zfQ5Q2GvJ0beipebNfIdKYgu0d1pxMsQkI+saaIJJxeCzZmMjmmQ9XDsXTfII/YVJBJtgg4NE5EITQgVqMN0MTRxdNorBJKlir036GInoCM2yn5TudEBMveeloLt6ATb1TPXojHOPNd4LafZTSR8GRBDn+nJtginXRyHQ1hQD0Si4qKTb5umSmYTMxpz7vy5jKi6rhNG0/dOShCGuhyUhgP8NkFP8JJdv4S1rlDFGYNovqyWQrw2CKDGbZuaS/TsznEfuuADCqjkKL2JWAmS60qvl1cl7D2pKNT4xMcfju4efNIpjg8xqA2p0owNtxIsrVmUmADY+pANyfwsluYbJctRx5CSkMWD6P6ECIU1yvUt8ZxuvVoPCH6KYFcu8L1eL1Sk06IMASTABcHLZCBVo+FQ9quWsNsGU3JZilwSHTZZCpeDcXYWEzaSBpODCTEdJONq8xvktZx1mD18RAAjoYYEGAVloAvychb+EJUtlWR1PwcWcJKIY97aBCLg8NnQlhjTlHxiOjKCpMuDVnICiX8xCFPAKYrUq+Ekxm3Y0wzM8mWtx8ysRhAcCGSyDZiU2WHt37xROFVFNjyHdH5OSeVmokWBLvQ1XahL+U1GOTON/dXYQpegX3Q2FKUT1G+RyjmTrZIczxcbRUwejqJA07AdRLBy02rihGRJCLv+AfHmj8X0iJnnGRxQseJOXYylPN0xS9PZFI0aRFRzFfG9HfHnXc36tXtg32D2P1Qqo47AU319nv2jrEVfijB6RSgFAlkBykZRo0zbHxwfHz/fixgjc2bt0o5krV//LBEEsdEZVFtScTEVLaAQFgzBqwnJOowLJEkS+COjZG0U4FeowNfkim7N8u/YLostu5YuME+TQJILgKoz+gzXCD3coXtICW/JiBHiEYdCfrV1b8o5qcf/vCHgyu66JlQieqesu8xHjZZarpDv4quKmJopZ+ABshmI7FQeNvWrZKJ4yx0gs95t7aHQJ06FI56+4YZR4+csHPnUCKSGR+5fU/2vikOpg9393WzlyRchZLB/iE2CYE/zEsWQ78SJ8N8TWIICUFkAZ7LhqCu7bm2rmMW+lsTilka3VJUPSwHLJju6kLZlAxhXk+PnLk7MZUR6CaerLGV/gozAPB3CUukfeVfi2udhEUwMbnOi9WSoMVLXGIzeY9RD4au5YnbX7Mliu8VeHHtTv6cxyBEEKIvhvwXdvMI8wZFV8qyG5QS2owynyaHUERy9g/S02Givq4cKWvVOekeR7oH+plR4WF0l/c4wunibI5Noyj3aFmYwAwP6jZT8VVXEyOoMzggcsHc4TiA6NB+Llc+RidJ5l5b04pkcTqu/Be9ScyajsVkEGZJLnCCz80s9e655x4+8WM29qYJwdK745hGKH7HfcP7JyvYG7u6e8gNSYfYQh2TFSzJ1FR2+s579iDWt23bRhEqMWdJAZGIUogIEoQpunZIdv93cYAxv7ldrDDqMGApOIENVQaSIVp+zAomfk+XrLohYNNQb6heykyOk0rGCSFJAJHuhSjcbotJl46K4UJRVusSazjn7dl9Lz2Ax//eg0ugLIQ4xddFkvi6wfX0tFMjtCZucFojBFenwskdHZlMxivksqlEYsOAVEQrzoEcQMvUZIYQsA+LtURmKfNktlKq7hja8LgHnsYo7tW33HX7WKGRTHRzwIWrF/b2PrbECkeKhelsvskbR0mzn0BEmoxZWtPIXznN1yWcIb7gZkFzP89BX7Ygh2HQCevCUO4cWXGiMOyjHSVAGpcwWduDpRrLM7oymc0HYNRoAnVaH/FpU0ne4tHbOdcgH2LncrLxSzIgZSaqNpuUC81QAz/Jf2pqJobvgzPsOcJpTjl6OYxX1Nkwlfj8eCL9Hl7WJmEziZ2FRm6prOxmJj8K8isgXn6rxdErKpTrd++dnGSkISJbl/Ba8M7nC0WGMuh1UUv2uuV94xvUNfCEmDMOLJoDq+jtX3Qd2p1QZIv7Iavm/OYVLQZhFSoCwPVqxCmoGO6Iybdcd3Oh2BEf8BNlrlE7dRsTjQWAN23cGk327D44ddf+TI2DiNx6EVafsKFvo1qmO57qSpcwXO/axce/1amAUjqCwQkzLlKImGP5R29B7JWpJKbWxnSu4ASedN0liTq8zMR2ANxghC8s9lUc5U24ZTmsQeKWWJv6e0OV0tTUBLeyqxdKtgAwQhfNrcp2yWAko7QiWRHRWD0jMlnp6p/fkBkfPfv0Uzf2gy5CD0n1p342XZKZaDnBMJHCqhKK32dgi2wWey7gOjou85M2DQ6IbYBS3csLLkJOZnKSQslDCHSG4lo9RudiKJ58+sNOi5QLv7hl180HMl5Xom+wi5Uz2qLkk051cWIUCp8ormToLIyo1HonWTVkLxHAr79XukQ4rvQe8Oi5RiRU2knNI0VHiGGwgBNzCOyiJcRJy6RTKWpdKDHcLyRQIkk4tZAzM1CwnALs4kps38M/BWDanVQ4raV7p9z9/VyakdBWAWBdPXW46Eo/phcoHHfHbwQxYYLUS6rH7iIuOBSemMzw/kAS74NwQCK0EK1e6XC6c5rFQiLkS7/Nz5cC+a0iB33MUfjop//rY5/+Ou9IIt1HD4p3t8SKBa+BrYUmY0IdrKjVKoLOzZqsojoYKR3FgdX1AaxC1qlU4kO7n29Nn4JIzg4rkhzH8XahRgVoc7ciejhnRoBI5JAvetja77RjtwJ04FZ/v9fdO8CZAjffvbfYiDI0TExO4GEkj0mwJOjqYVvmyMGDB5GPW7ZscTmBQw6/3I0gr9wCTainsvsVAIywQIFzNIjogw78xCIh4mOqIqoq07XkMFgHJESQcdZQKO0OEERsbh5MM3tramLSn5srkhSwpRoIJSmdJRkuf0eEAxuE18+vuorpLOc87jE86umNsFSUKjRTCg0AsNA27cbauKd0l8GcC3OGIBX5Dh/3759gK+IdW7aSlUbmisaGUXpqMgtvicOUoDpwxjTsSigV7eqLRU/f5g0kI3cfnLhh78FKuLFp84DUgJT0hrBC9/YB6rmcbA8pZPkasHjFwX0HwDXZGZQ5wtJyXH0AdjthkZP+3DRo+ihMzxG9GS0XBtEEzuwrmcHjnrTs0sXCJ00CHFGQ7BXqhVOJuBba2qY0FAmZhEygmj0lo4U7zv1UAJbsZjsKdT//SVMDzgoxQh01lrfF4abUK5PjFAzGNKJszyLblx/KSV40uGQhPvdjejr8IIGith+qMVbJlVfovgNT199+70133Se9Sxn0Rm8XezufFN0X/Izs0OEQDZjH5owDR8cB+brMHY4DTjAx3in4d8ifRnCfKp8j36+TPCpyalVGihDxTjMUcOYZ2xnwDeu5ruhezHk9YUM6jnWu1khHvQ0bN4eiqTvuO1CsRjZu3AhVzkRZZ6UNmXR1d2ECZMCSVS1s5SQlHdpBFKXJGC1HwXMliUMXpCd3WhER5pB7y13j/3PpVdfcIuLGESZCB1AEqbvdgCT2t82cABOuT0xNypxXnMhiRrLFKOruRRghknDMZWICMSL217ftGxk9cOz2rWedtkG6BYzgxuS/QpEm02FUN7xKnqTXn5ChTjo0klT6Daxygto9e+5jPc72LZvhBoULKoOFYioI5d3ANQDMHCjWxRB534HxdCK1uburP+SddfLx0434rXvHpkv5rVuGZAo1zvWDhrr76EmwYJesRI+X9VTUxuGwK55Ky7nF7JXFUK6jEv5yLjBtUSjJ1DIlWNKKIwrUSWAORTscGuiT1cNlWYcsNol0VxLOZ6aZfKZj2PIusKsSnbMUE7Jd+oCxJFE/2ImHhE2W88R/JL4jcME6Y0ctNVDntyA35E8o98AMHSNV7psIylNNIo3Itic1Kh+OMlRRLMsL05rLzI2koGX40YYUy09VYWBYbyVoJq1P0kr+g5jb9xzo3Xpcon9zlo4RbwiclzECWafGeIq8izQcGjCLAvnIVpJYK3stcID3ytz9cICPDCmBQoVCyQ8Jz23gVI6oGGmKleYMFySazFYNCRA6yBWBxFfLR4skZRplOhVLhOt96L8sAZIjAtmYqQ9YGp/i1NrIQC8LRlQAkn8VWFBVCsWaTYlJRT7krAJa5bKIA/4QdogJJzLBXyZMcQ6sPAIYmJQteaIfSy2AjJvvuveiH1z28+t+m3XLZ7E0EsixTJDH2bSQRBF9CZmPwk7CYkiWSnAGMbbrSA3BKnWTrYb5h8sXZfLwtOf95sZbkvHYaScdy+EQwL7OTC7mc8Tjp9Si1ZEQ06sQ52fQylyhULlN9sh/ch4fG6NPs7G3F274SeRIwWikHi4XZasm0riNsDiJ0ds9PNoViWzqibEK58GnnxxNpocnM6VCbsOALAJWR7EclYhpnrMWqBypHH1SMWW+lIsyLU8bDNBqIKn00MtSXWbKSrmuVsIMEsiZfQJUpOJpXzpNKt04CU8yHofJuSID5a6ywCoDwBgzI1EmzEkOs522LzYPPM7NfnzEdyx1htuYsjUF5DWd9H0gX18JAnWrxZmNJhzzKboZn84E2iAjKxG2x0ILVEVQHhOHn2CtOCrIZyPrjKmWwDA/HukHJdH9eC7yKrlA1kS+mm/E7x0eH8kKyYSwAbvr+siMdurn2MX3I6dqmjMOHCUH5n/yR5nh2kuO7EU/FANatZRHXoIKtWq5Joei8mECH07uYJ/VX0gO7YGtTMAhRVfTrkgIEifBTBvZvKm/kM9OT44+6IxTwiLDyywzQTo//IHHNUqlWCIFeiYiIUYEGWGVzBhLZix2wyBDT6yF6E2nkKOoU8ze5TxZFkmAGBTeIr9ZGRtB69qwoRucY58KpKGIRy9UlhU3SA8mSoXw7RnLxAe23LHnANtPEEd3EhyfzFBBTiygLaEWyjcM9HT19N969wgile2lpir1sVIlFEuhATJ3K8URAmRe8dJ9sqhm35h39TW/YXXPuU99MmRTFHAFeT1dXRiHhTNlsShs2rSJk3EZCE+wHkjgT9Z1BI5y1HEIHuFYbLmyrxb6yCnH7YRWpjETAo839NCBYQuw1L5JSYHMp8EESqFzYnTnkKifv/fIB0+MjWwY7E9Gaxv7ZHJZnXFX5zYO9NNMBw8OI05F+6VgbAMsaoIi7sLRA2OZUIRTEePsEgnP+RGLLRvZInticlIY63iLdkQdcQ2gzhPejk5OVWr+kVY9sqZb2ofzLZDjbOpNqqIs9a1jY8hk8ywV46ReIgiD2XWZpqJ2vBWuz8TwMLgIuxjShzxePFqTbUeBVSnyUI69OCVtMwIaMIaQwYENDjXIRchWR0Hcg/FSHIdPb91UKJULxRILt+QJ7yK7MEZD9L9AH8qbZKNKOhmhMCd03b1rr9uZUagVRxUb9ZKzuMCZ8VzFGXKxl8hCMrHmE48leE2qCFhBByMx3rQSwM2ByWy4u7eWSHFwFre0EPMfM5NTclZVguV4Er2rKwlPSOrvz9mahfmNAwvhgHuhFpJgncUFVlkR6bTbUD3KgTDoIsgQNt/jcFpG+8Iy4CfChYUv7AQlo7z+AkEkFF+pqEJ8xlUEkYg8hBh2rXJxuj8dT0frmwe73TQpto+I8z0PMNiaH6+V8rVq8bjtMspbRkJg6qrJ9h1DPemueITx4BOP24GgRMvjbDvy7nJnp4sWOluccT4cp8VJHwHHw+Y0K/E69EK+TBXr9PcnivVCMzWBiHW0ylRSpjzzY1oxdlTWwYaTvQcnqqLWcOxDIlWi7AjzQt0r1KixFQelADy33r2nUg+feMLx/WmBK8VVVR0U4JKy24RoWv39/Zg68YfRLfg315GfvysFhebKspkiQ6psEMYenOqoIx53TEI4X3J6vViPYyyVKVRqkUYZtTWdjMChh555anliJB2qb08LgsquxVSk4dKGZFYUlPNz1eFUXpYei5MmQ/1lcnVSjhsgUH+iI7M2ixXPbudLJV7aWo6rkKSQzuZWREgjtaWjILlRbcwexMoVGEblngCZTC5mAI4TaqqnEtU5Xh/1gI54WtXQORH09hDXZg569LJqwNRRONviXDF0BYSbmKApiHZhO09I1apxdQum5OXn9Ewx3rNJWaWWLfA6S2XFwU0ulTJYhY8KJrtjvPe8JxXRmFn7xTJnSSg7mLrILtmKXeAqtv2geGpNRTKFcrFOPyOWZ4saVzWuMsgiXSWmVEoXwqXCBi1taM44cDQckA/b3P1xQFUhiQGvxGQ6Xa5mi7ItMpKFX6ERydejxXqkXI8WGlHOjgUV5RETVJCqGFpFGUPsyoJg9FqGBCP1ajxUY8+/M04+XmzS6AmIt4b3kJ3esRt7+pLhvq74xj6RYYgtNkGkXNJ2J2KVfKY8PcXCXMkqyuQm9h4SkQDAImkR9yIw/fFU0ZDigKc7Rafk1F9fUCJYRLGRio3lUOqjo9PlrNsOmRBUnHyxEA3X+1kw4xxwtWHD0ESusOvAePdAlD0KiHPrWO2ug8wVY44ozxFUIoywxDKn+Ze/uQl78MMe/JD+uGOZy0QoRISJ+C7BR2Ij6FPp7onMVB5wQi4L6b64dyn8i+hUEB/yDk6gxE2zezPmd6pPZKrjgIlj/vqR65nsNIShGbKFVrYkhvRYtMIpTBjQOZD3wSdvr47ch4re40BUU6KB9Xb3xMMhJgkrADsWyXw0cBF2csuZg1gMmHVFofw0XPifSNA+uo20mKB55JPs/8fOTEBfOiX15TRdWXRd704n6SFMTcuQswNceniNAuaEcIyF0cQURsxzwRiwPJFunV9E4JmXQmoggcog3ka3EbSOu8+OLNYIdYzrwpABZqU1GlgmmBLmBkz8p/TaoBm2ywwyLAPJdLFUZiI0ITi9yuy/GKvcZRo8/KQ5do17tx+QLbP5sdmWDrHrVHOaNSjaL2OF/gn0OvMFtRjBHCEznznCRPoIoqMzLlMpYaJPyoiOkBhjQxOOQ5bPVt4Qc8aBRXNglXwCi6a//QnRa9lDqMyyPxCLLZ/i0XgchVXlNVfUI7rHTENi82cxPrK3khPcTPvlHCGfvrrY5VRIIbuZ/BypFBvFzNahbpHzokxjl5N9iDanG7mRPbXpDGKbkVdMXrI1I4cH1xpbB/pSoUZ3Krp5oI+FQuTjZAPaNTNCNG9CyE2ls2AaqiYyGtnv5tmKlMSaKjZaWUokasoYB8GHYplSfTQjoKiOGbPsO9jTLdtckobr4EAf+1zuGh5DpKKM3zrm/dOn/utTX/rGqK7iJRqFOSsxY5u/veteyDn9AadCIT8lx0EGMMomU5Irf+AvqJDNTNOTwM0TZAT4lXKLmDgs+QCq+daNG1B/gxlo5MOPsXM2gZyckom70kagJgt76o3urjDnH8BbePGwE7c9+uRtjzr1ZOmTUI2YbLsJ+PT1pNHYMCbTlCSUIiHTrVgVr0z6zXIkIucxS1kuBh5wTRalyEEOIqIJESdPYYS0jCZETMtqLhkdkA+NN6UrmWD0lNMyBJxxKF2hOtOa2JMMhkgk50T2tzgA2NkxZLhcWhRCXIQjB2A9XyHRxRGO81mtQCjVhpy+PrHcjE9OUJSDJXlPXLGufo4hvBR9A0NweHR0nKd+nrIXuKThr1CUHtlYyfvshf/zuf/+9m8PVDGxwHUGH8SiQ1GuDZsdCUm3gg4Apso4aJjMMgMD005kKp/TihNYKReRAqkEX6TwiqORecTUvBWk2YpeGxxw38zaqEpbagFe8OlFq6EqWx3LV9fgw8NuKT16JA1SRmRJ80cEfijHI3mvUPW60t3+1gfhMAOlTB6t1thaNrER7TIW6utLbe53RMsWs5y7x+hy9LTtfbffXt42ONQdE/stsoBxpni1Gk2FdmxOkCoVjZx0/A6kuhPz9XoxH+0eQChI0QKW/IlxFUcIDktvuZBnw6m6m7cVAQmQgLLtpIxTZgrVKqU3IvtGpxqbBGPIFnWQVY4chURy4iEvWRsKAE+WvExdzlH44rf+b+9Eef/de8cLYjYX6inNGWOZ+TuVK6S7U1s2i6EZW6cKWYYj62xxyBkGsTh8oxTOKgbpOWVXEiORnTJBklnOMVQGAaLe3v37gDGmQAMITFzCoukQX+BhgCHZxp7xqam6t9WN0nqZaTZ+DPf1ArPSHOTwyJNTPS9+VldXKKV9AjYhYSw1HO7tZlzem5yaUHZBm/BR7KT+yfOsiqZmvQPCHOGpi4e/K50eHW2wRBgtWtNKTfE1lc5JFhezmgtTPmGywwZ9n3IyGhd1tlLHot6AwXW6bWSSR6ckprSplKL5Oah1AW76j5wH7D9wgfd/gaFo3ZEQ65fkHVUA1tVTsxLKUyo9w3j27cJso/FbY/r9OgHgabZY3b59++i9t46wNNs5YQ6O3OjesecmR9bzFeS8n994G8PPG7Yds/0Fj6KMJHtfVxopGdxwdnA/mUu7vBds8r5B3nFb0ZfPOTtdqIdT9HDcPl891JpGqclCu0awTky3xMoXWPBmzjhwVBwwAL5/9oXRujiKPBxnz3oB3SuuvuWKq6/NFcpTHEvLRCz28GWANhxhj19GIkG7dLqnihoUTdxw253lBkPCojWpY1pxIpIgE/boyE0cPOPEYzAug9M9YousdyfDec8747gtl8Xq3XERznz5ROYjj3d1i2JRDufHR0uhUk8qJLsvAiu5bMQd7McQI8q2SBKKls0ucYJM/NjQYzoDsWzjKKsYpb2BI4auQ950jRNza1WmO0eTu/cd9E7vU1JFA67VOLwWR57s5cR5gtlSDbRGZfz17aVrbr4zmu4d3JhgcrYWJkIcG6sXGpmAhNjghk3Yr6FfBKwTsgLkohTxv8EMp3CUVZURBBlykB2jujnc7/CyGESD+ffu3g0w7Ni+XSomqqASK140YJSYycwUQbAepk3mqGqkr49JS4A1KRr9odAjT+ghH04WluxiXMCzMF0LNGA22oQ4+NPM1PW76Hqw/VYuyxAtGrBjh8SgRZHLvgY8zQLiXnnU4mAEjEKCE8bWVwpdUAgrWJhNLcLVKttZ1AfJrcbkAdCOp6lkEh7wwy9N6RyU818BWJRgP9j/B2w0I85+4O54M8Xe4WIooHY1d8Kal4qqCnkMidLtQxcv5spTRfbrpg3FUa60oOMPq49Ifsxxx97yqxiWavz8ZlqPVo7UsfnLq8sA/MCWcixz1XU3PeZRD33QdpmwxeEQivdCGJ+HX4KWs3zXgMV4FH3xFKshOaw5moKuqUym4W3Td7gsBvw6o/nc8p7wmfO6FtxhlFTCnHFg0RyY6fkuOos1nJCvq+wxs1cm9/JjJHV4Mn/brn3slTE8VRydrnJIX6YSHis1hifLuw5MMp34mut/e811N95+1z1M3RnYMITOUQTkisV8Med4jfrpDfZ1bxzoe+Dpp6DdIuLZiBiHfGtU61v6kpw0MNTbJWjmRlZ9zaNc5OCA00456azTTmG3RcmKDZmx3spaIRYayUoj3xjqxoBBQwjGgRPIl2JBC3FBImobGNSnsrJjBepwJNG1d/9+J17dVvtyNq3X1y2RiYt/aMOGcCx5cCLL1NCvf/u7nODEyPGBzP/f3nsASnZUd963c+5+/XKeHDWjMMoBBSSQQEhEg5DJwTasbcAY47VZdr04rNdm1+vdj13A2GQhskhGgFAE5ayZkSbnl3Pn+P1One5+PenNmzdBb97cmp77bqhb4VTd869z6tSp9CNPvkRJBCSogiyStHbuGnF4/J1dPRTrkL6lgrncN6jB/HRvby9c7/mNmzFtMyWrHKqsnhLxq4iUe/fvg/G1tTVLCqzuEkFenhIQ0MGOin9HURiiSETNWQrIgiMx+3Yj8SeSzNmixJC2JBRKDnaFFD8e1N8lQr+5LQegxtRK4S6RSiHTIe+Sr3laOagYhPWW3q4cIQHEMA0q2wyTfsjQkQzFEKvE/hnUggHQhEw6S2D2HwNl2SlLZuwPDYoTtTlgTZlIel+R49B3qtf1eI22n8jqCrr6vPK3Ui+TtOINU8UoG8bHUzpZe0h8JFpaoLuLMZ9vbHJSq2FqDDqxd2MG8wcuiTQ4wcYUnkBD676hiX+/96FdYzL5IYM/k6KxeTok7dN3qZMC5AcllYw0UCqFxV2Ba0gnw9BqcXKZNCM5TPmgFT+GUDzRafVqFPuvTYG5UEAkIjvMQAGfR9xXJTMlj9/JQpQrL7kQN5DRxmav3+f2ycbyjIphrRjzJif5TtPoqXCv6A8EMbTxBMKXr2InWySYQNghqlcieAPhmy5fiUnzJeu6oD6fMmtwsJTNZdMhX+ii1T2//863rF7eDKeWAK+Sj96dzSR7moMfeucbWRTRHmR7gBxMUsRegelSIOBmPZCwUIksbwpXwR4JB5ayvJRN8qaFJzHPRmJFe41/DtxJslGPJzjCbkeSmwO0wbqVwT4iLOmhSGQX+Vg0BnwODAz89NebdvUfWLNoxerzNvz7XXeNpmXyme2EsAgGtBATdg8Mszs7O0zUd6xKwZD1RH4os5KKZNHMruhu7d/50satO6+9aI1W19SZfHXUARyYOXBUt6xpGZ/wuQPxsJlAFF8beZdLXEcRFcPosruUF5fTUmbmoZPUrVTwY6lsyEE0l2wJWCjn3VhBUYpCOuMWA3TUDyIBj2WLGVm4Y/IXKd3Q1VyhMiUTv8+YbnMqpmZSE/YJzLHTVEFmPoUfkzVkNQMJYhCKxk1UICi50Bay3xG7ITNSMObT6Qykoxo8LOULIFbR6xLNLDkIJJiWhA5iLydNwLofWQgnkr9o65lK54ziipcYyYyj5m0iUGDZzJHlc84SMjZEB1fYUBqXJSTHTytq0jItx/smUxLiKRsiZZ2lyXQJmwbSZVzAj4RJh8xy3GUVWcwKOAsYmlF0sysXiVEolxUKF7JCAPpPLpVNJka7epe5y8UHf/v4ZeetXRxvo8aQSylmxl01mJNqmPz15LBjpWqH3Z/1DRIgmMaRRje1kTvcl2+z6Ehm+E5Yo48xWoGhlcan1uKIg/GusV8gpuzOxai3smGUpGAHmwJzo0A9n5xbCqfpLdGnVdcMyEdSlZkYt7LAEb+GDEgRSmBehNrTEywc3yqsimOjHz4lX+mKRsfyxi6SPYhtEAltcqNwKj5rdHjmqRi88ER4jQOokBKHAmG+Z2Sia9d1kSx3OJpFNU6vILVEvnyFEfIkHWdF7HJ6fA0NJLWhW+Lz84h1L7IruRJYE2u2SSA5pt9kSo9bgmHgVphtxK0i85HYt5ICi4ODniDGNhiIJccGfKV8Y0PD/v0HBlDJAr8O68B4kbyYeaY0KGspIgVGc+krltCU/+SBe72l1CfefOPOvkFsrHeNTpGZU3TLOcsTBgB39o+40hOr2/HeYdTdTDSTq0PMYl1MFpttB/PpKVcQ2yfrNZed89gDP3/46Zfe8fbXkh0OPhAyoMDweCLeAJGEPtCdNPpSOacn4MxMtoXF6g0TVJxssIEBS3eQTaKtDanS1OS4qHApcyBg7dy9jfmArsZ2Nh40bUFC0tWZ0CcCwx3B4XI+bBZtMyM/nm/dP2a1NAttRdtvwIzulsKSaAIzMW8zWxRQSzHfyrsdLGi2GuJNRU9j/1iWkrtKGGqF82bVNnoI8Ae5cHx0wudzNzSxvrbEfr5+L5Pwk1C4s7d96+CWdE58HApxHdbI6AGPq9DT0Sm9CoxEtBQRXWYKuEGCkaAHs/n01LhYAclQquBE0AxEZcK1hJwqK+JEt55NWB6fyyt9jJc5kZgMEFlvNjHuc7o7QwKlgIdYoAnwQJPKyIzYzAwwjKM3dgcL6cFk/2RugnGS6cPuYsKBtYCngbnQ4fE0bdEbsXobXHsnPC/umrpqSQTSs7jX7WUJU8kdkL4n/Wdsjyc/2tu0Ydmll9z5za//+O77L1v/Vj4K0jSVc2azKSybjCWalMcMYTiac7k+OCgeSgsdVxAym35U+WZJXdJgRJNOeWQGRz4YSOTwu/fvGGPQ4Yt5MczbunOX17qKPOnV4/gqK+XiEYFqYoaCLpb3ZwuiTNHUTRb2wabAcVPgKH39uNM55S/U0BckrgWmYJkeA33JHvQVp8RwuJMaSA4E4QevgnnBGflk+THAP+Kv/mnIxDQFEjpryUhK0+FImgJPlQJLHC7JwnBJucslP8EDFn6Y1OqeVgzE9H0SkXTkP+lUkuIVvFiiPcvLTuoSoA9sF5Gbc9wL+Bzl9uYmfzCMf1+mLInDCisEr6hfTLC41LfItCceyY4P+QOeG1559cpW68IlrSzM2L57DxFcXr/ky5ZJOBJKphF3OmLixqsahHHyX/5QY6aIvTLk4NfZZEX8/rwn8OJ+UZ+zaQHoi6vhUINg+Z5RkcBImUHFjr7RZNHZ3toWZql0JakSGxlRGaJ4gyzIxlI1RZqkQ4QsC6/xpukxTq9MxgiV/PRdI2CiMSihDKWaQRZhW96JtMwOSBQVv4VWUv0EM9+FYsjvM6bXYDNpIIBaYD+7RmbyYl4GdzZ/hDszPuAtXfiLpMRSbNkwijJwl3kFq4hTDTpzAqUK9BDH0az2Jm8218VMTWKZIN7K5A1zQXY+rxsTPvI27iiram5JD+8mTEDQqoxwxFkXp2laQsovvQHZmStEbzQTlX7F8MvkI2+brkI0RG0owiARGjb4aYjCVFZWhzPikIAwLXtv4DyEBN1etyeC7VvQzX7GE2n2P5aCkhrtVWCnIBrX0KmQSeBFdfmizkvObz5n9dqXtu748UMv8pT4OkHind5+grfNAznyslwdFJQQB92azQV1qkQzGdS/UjJ+WvWLkI5BTVmB5Pf4m6JR7N4J1VdpVuiHS/bK60xo8E2xEL8WoT5d+9ymwOwpIJz6TAmItkAvzIs5GI4E0HdkZMTMronHY2bRkIMRf9X+5Uyp1yktJx4kSF/dQcNAlCnDmeEdg0Mj6Ke7uzqYk06nEiOj49ycSopBEJ6vdHAAO4ZrA8DNAVdpYpAp6huvvpQN/bpbES3z48NDLAs2wiUdycm05tjoMM6+2lrgz0cKUgJAwgvYkDIuNToWLcEM7emnnyYXbzmLfbBYAFnWt+7e+K93/qQ/aSHgJhzWlgOjzLU3di2GUQIn7C4vgwxEVNF8s9YW9CjR6DBZ7dDMU8JAZV8BqbKZHT9CcSSuvB6Jgv9sLUBkQRIA2JzxiOxGJhLsatUcVWjU5AWlVN0CYSUNVu5IPhL0mEpzV1g8ZJTkeI8HstjbGcGRmdOJWyvQ2vL6UUznMG5DAvV6xT6pEuD4mpKkS0XBWda9UMGKERP6EnFtgYQftLyBXNldYHAjBvAAdxn1PiUUpY0R59O0Eb4uXcaIiHuVhKtZmb8UUFRH5pGsuXLI2miqzw1JBqnUmEuxrzCP/DgaBYCxDHc4sF87DNskRcZGw+NT7OgXDYW6ItYN11yJyeIv7rnvwKQQA9MFagQ1ZAQl+gYhEBlVfwaDyfuQnynqyThIkcUfCMGsb+aarPEZh0liZ0c7NhL0nyRr+qWRxKcK3IbRCcXhHdllW8ZY5pk8t4NNgTlSQHrSGREUfWtciTJzh8umpia+DfigOhlQz0pw3jOiUqehkBhhkYsxnJHcEIcxKdJ8h8dG0VS3NgYbo6jcysNj4zDcqWSWUU7MeKIGPLxIbEbyxpHFpecsvvbCc5Y3OlyAhii3XSGfZ3SMOVISlXle9uvF2ha7s2i4wjk1I45wLgJxEBBFAC3JTsmE8y+4CCa9cdOLbqTDYhonYfD5PVPWnf9+388feuyL3/zFi33iXHok5Sg4A81dvfA8PCwDSbJkFTmQ9Ujo1iMs63WxgITyw0mR1KaSCfijdgPh4RVINVkefOADiDdEqQL7BwN/TPVVnhvZGsX96FSaMUyTGEtrQvLJwIVZNcQJO02ZF7g5LTBRzSmca0M3Y6MuEaT+3EaKdkQD+OPKTU4a3HJ5GU9k0mivnX6/2TOZiCZufVdH+Q8eiKBrZFZxh+ryQQqRSR2+Isb2Ru4U1TI+PRxlVl9RQhmemFzZl4mFQ26fn9aXW0cJirGUNBqL0UZYlVMrLqtT+C5uYrjHAAt3IqTf1NiA3Dw8MqZpCn20iblGteC2JnBoWQTThQCXnB+6+KINQxPJBx99CptiWtl0RXojErM4vuDESOEmtiRAZXmx+hOx2PyOUvhj3pZmOyTIcKM62jKPxsbGkGw721txegX9WUcmphUGaxnrG8cpQhAEAGSBaXfZhyRrX9oUmDUFjtAtZ/3uaY3I98AXUJvchb2CE2AwHJ9Heh/0bWxs5BEj2dNauHmcGTiBTAQ4aRnhN9BNudpUIsklXopbEXh97qGRCcBgIiUbBURDYSAW/9A+/FRjuVSwXnfNxe9/y+tufeWFwA7Tt2BVezwEZPQPiHcOrG9IE/ZVLuTxWQ13rguGMZtr4gAb4CR2psThwYYNrchmBwYGhPexUW6xjET5nZ8/O5b39C5f992f/OrbP3/sqb1W/2iy7Am2di/hdSN+GE0rlmQC/lbYxS6tXnxPTpotJRBn6QlUUwGYCKaXq2lUXbn0dhHX3DH8q+ABm/IwPBAdchWm+sasRLbUFA3EcOIkj01imK1hvRXw0v2SeOKQJCGkBD1STRYlA0hh4/ii8kDel4I0hPzsr4wzcKEG+9tn2X4qi7pZ5TGJbILG5sjPTPIi72LHLQTMYFflcLOKDMpPFV1YpNNw/EQg9gUZkQhtqYJoywU/JlNY9npcXmNHpqlXj5oLxZKMpISCeFiVUzVE27p2RMqXpWvjeEXGQj4UJNnmeCOvDBrFCe+rVQav5xkiibdoK5nFPi7WEEPTYYUs64rLL/YEo48/zUYdUixSoPzgLrK7gWGQWDQE3CcRE/h7xF/l8Un4I4MLKYn8kQ7MHHCmtZH6MXXjmEiICxcIS49SADbNjx27bAVmA7ChmX04IQoIRziDAv2eeV8g1nwPSAwOJDw4Beo77iN76axwZdXmGVSxU1ZUXYaEBFzjMjJuEcaH9CYTe1H0yW2NOH7e398PrxlPs4mPC+gSjiwAgcCccxYzXQ3WRaubgNYCzn4Fm63ezrZMOrGrrx82WnKKrfjAwDBA1N3ZQfpH61hSDP6rjVjRavZai3t72Ffn6Rd3lp1hRzDal7B++8Rz/nDsxhtvvPyqK+956OE7v3/fju27yrlCR5ukagoG4iHNCZcnR/h5yB9gXdFoQuola2GyWbqKAjCvVAtzGAZLaazGhhiCKV4VBX8MZQA55muBgl0HxjEt6m5hS2QjAZOSYLAgnG7XmEgJYc0NkdA08HdkYhK/hfFImHMTwTwxtY6Hg17ZziNDdoiwrG0DfTHRQqlcial/BEIrASynOgixDKMEVP0+aD6WMqDrks2iSQc9gWAwK6zRR2CiZezbkXxJBa1GCXfbXjFSmyEIbJN+GWdYMbLTUSy1NSCFDgEhFSXHJFOhDD6IjAQM6iIBax2Bb4lMEYBiFvNYVt/IZNntiwbFYpBHXW0tBYdncBItNj2GDS/kL9H0VyuYFpJRkDmpPT8kVi36zCdGa1wXhWJo+qZW2IhX2ozUOYOTsC1KU9TZ2BCFt4xPTDKFwDAC52U42FQRn8R8bvye2wBcR1b7dK4UoEOeGQEVnBYU1lBRBpkR65e+9KV169bBMi677LIHHngA7VAt5plRsVNcSrEydZTZNwkWowH6cA4HBzxwOdkcs5Z0RNnYYdf+QTjsSLKYdbjD0RiRK+yJ2A6HK19CjsFi1cpnFBoW93RinbRvAIeD2BqJSNTX14cKt6ezjVgVNkcqRgUpaRguTIcT35Nw63Ix7JTZ5asuPg8B9L7ndu1mRbVl3fPkvqlUevXS3svOi3/ofa9fuWrp9q1bE6ODASvf1SjMmhQM4iBDiU6SZAGDgA8HTuGhSVM1VhkVikx5RiKi566WpNJ/lAiCo0bgoyyxSJhONTY5xSNUi6a6Egtq7DowjMV5d6sYuFeDfDKkGYSwwF7WzAQKKqOsBAorIhuOqfG7ghcOiURgLIPhrwHgpmAAPyojw3340KIV8AqNBzQGEBhvk7UEQTyREMmFH2lSSNE/O13sP8wrSKLQ/N5Hnv31Y3v6JsoKvWgO2GTYaATM8l208/KTtJJZ1nuDwCRTyaKSkcmNg5AIO67qXVlXbeaAq881itiLsbiZgU8YE3NxUBrlATs7USQlrqYg7jZkpyZrYGycpTyM7WggnKHjQGaK7bNAL0mvXJP4tZrco6ZaX+KbglYLJC+YOwff0NtzPJrWp5qUnB8J85uYmMTWDpOCZsZkpRJKEbmZpN3czHNRbi2qzAE7yogBJ7E4c6yF/doZTgH6/JkRFFb5YPgStMQHDhx46qmnPvCBD3z2s5/95S9/edFFF73mNa/BcsReIF9rUVqXfY3AS7i/skjuoB3kXFh/OsPuwqys7WjETjozMDzGTdTQOax6jPsIxBgzBWbEvlLeVca1UTEWDE+aneraWpqZ4xyZyolYJsbVlvhFKhUQKIXTG35q5MXpPgb/QlVt2k9cjLHICqlqw5peZKZn9k4OAEuW9fW7folm++ZrL2bbokWN1rvfekt7PNDqLbcFytGqRZiMA0R2AbtEWBX+7vGVvYGJbAEcQgLWURpbIREYuRlgoIBKA7lZCUCfy2KHJeIncUcFFwbc4LOmyJBsaCLN7oDNEQO2IobyDD/MooJmGMEJ5jngtKwCQkIV9bAE/rB7MhEBaa08qbK0VBOO+hxshNW3d9fAsDVWZKIUna3YOEnGtUBOdRKwPmJwSZGQmNmB6MVdk3f+4Kf/70tf+Zcvf+3Rp/aAx1K0mmMyyVXpI8MI9hvmGaJxpTC1XKqYxw2d0OEEjYJoTVxO9p6SNLklpJYCklQ2V0Dk9jtlBoGdFWnAyVSKwZ3srGgiC/HNGeujkbs93mAsJK5WcSzOyjLWo5edvkE8tSIuy0bLFUijYCSoPwfEkCQqNZDhhv4kLo+0EibCbA+iBpgOXEiNKvf4Yygl18xn0fdCXpk7gOGoQQn2dGyVQU3J3pRAJkHw3c43JSlNp2uf2RQ4bgrMoTcfdx4n5QXG7/AITUpnXzo7Oz/xiU985CMfufnmmy+55JLPf/7z4ME///M/I0rAGYkJWmv82szxSSnJmZUIu/2ABIMDQ7S00g+NIBwHB5CTqSybG3YELFxSs+wknS+No0aeTLuDOBppFfcgHuRaPw8EHcQrITAORluBkB9nyytXNibTqRe27IK986NtkIABs7bmOOyqQnpDrGkmVWbHWVlgI0t4vX5XsdjgsnrjVmtbx46k757t1tcftvK+eFvUd+0qdxTzKId1/hLrI+/5nUuXtb/+ivWgdQTkY/BgZDvKY3Bfsm5tbUcK3zc0TjWHRzDlTqkWvVzKwy7h9UcLwFxzYzCfyyYSTKqyBa+I1BId6dFlPf3Ciw63Z2lPh18kRDUEdioAtzQyuV4EkLDiljcEUaSiOPQgbN2xAyXrsiWLYNKIvtxzexECRRZkdv1Nr7k+m5r4/g9+CFH3DKZi0cZ8JmtGC4ahU1zTdbGjpjq8QnmwfwB72MVY6uLE4Xa25An4o42bXnzpa1//xt//3189sU0Qnoyy6J9JRjCG6ojzqclkGoL39HZJvaqBx9MfP9PGALTJiOyam73on3G1yLtUSZbrgPxmMTcD3NTUJE1MtEjQCkXCmAzs7WPpgURl4MWJaW53/1CWoR5iIi47uM8v5hFoHx2byLF7ieVGfsRQi0xJCuM0sU/jTf1ROH7MMLCgChqU3WVAvuwuzsX78nQtK2fVqQJ2TkS0JUOtJmZzwlhKObbRXNzdiRZ9cHiER1glMhUcjjUo5Sgw+jZiwl7mUpwq/e2/NgWggMoqZxIpkIABVBVz9+zZg+aZdcAMXZkGvvXWWx966CEqw1wO7osVgGGXILfq35C6zqSqnoyywrgR2XS0Xh2QCHMDVZnZCgdklVLIjVmNf2Qyxe5GLHvFSyPClopL4tjX6XOLbCV7JrIhKu/CNXEVEXBjCRxMFdxTGBaRIGs2MhnhyyHZ8X6a7dWfAmKYOju9shJJ2G4Wc66wy71y+eKtTsf9z+cHd+/Aydd7b78FG1+vs5RNZ/yh4IXLfGtuvzUW8zgdMO1sGX1qRQsikgjFoRP7PL4cuwwVZORF36Dp2QaDMnAC5xZ7YApuREBe4KeeKLhNnEiASVUPUIHvwTCrerhJKvQx0JQMy46mBszOMPb2ic2XiIKyIke2U2ILLDYTFC0B6IUls8iIRGBGdmIyhZDUEm/AAwsxyVFQuEwU/FdYxXNW3tvRuHX7lodeGIs0xXGdds7yRSB3lHRrgdW6BHPJEEJcjjA7L9WV1EYnGS54Orp6Vrav2Lx125NPP4MVd/wt11+5IhpEG1/A7lmaCa01RQJIGVawm7ympimYhAX/JKYJfCk6cGJY4PH7EAFRj7eStzyVkpEvEj8jgZBP1Nn0nEgkNjGVTiSZCCe+qL4lMoniKpm9IHBu4SW6NBBPoQyS5UQ6PclGjQ4/TQVxSFcWWGsZakcplknJF6LvMhXCQi0Sd6N2qNWBKLML1fpJ7MrbdArRi1BfmfOGRNAHz6HcwbCfRVzsoEH1MU3gXQYxOelxQgHKxE8spI0KuvZBza4gdiybAodSQHr5mRL4BmqKaPk8MhkWAa9cuZJzRtZA7LJly/r7+xm8g77KhakaoMsdEJr4Z6HhovG3iLNoZd3TTT0yLhq0aESsnZmSbGtqQF/YN8qMF04cS9itAikwXFkZgr8tGJUwZ7abFxbEKbJLkFnAhgbMavaPimHz+EQOIuMvoikm0SucbjpDzkiPHRDANVwkGgtdEitmSeeS89Zt27nzmRc279q1ozkWuPSceAhr6FKabZQxwCZCe9gZKKd9TJhmp3LZlFErwxAxBpYM4OCxYAghbSrNJClLZZJOp1sN8SgwXLVma3NQccwFMBALWk2xMELwnr5xMdBGLwpLZv+lYfxOlDwuZ1tTIyUnK9GvSgxqV8KZCF0uVyrjm4LoMG8kOCoE3cDGkbEJ4K6zTbZjknobxX+akYFI8NbSZuvaKy7eP3Dgngd+MzjGCLHUyH7DvHxYMEKw6HsFPnErKTlJMIt/nIsWL3nvba9+zzvefsEFF+wbGNi0ZTsDTJkGJxsjKbPrBW8k2WCAVvbqomRTHJPI9IFXVAA1tygIg1oGbWY7CbklQO6QgQXWfAikkaAsQ6Ln4O0cR6YjE2LRremiqJV6ONhoq0SBscDiUu7Qk5CtmZ5wOEcmAWBR6ZKIoC8v61Fy4r/kJy+JNhsHIti4O9l+hH0wVdNuYsz2oOkRW0p4pEC70Z+JNjYpWg5GojjsjIYD6JgnjYl7Fi0Cdif4IjN05YhnFYIwEy7sYFPgBCggn8YZFFTwRaJF26xCLeAKslIFJmxAX1E8mnGp+spnVvLv//7vAWYMpFFNc3IGVfbEiwp/YLs/hvsMPTQ1kEPZ3dDYeK5UwkwXNsoUHc4HsCLe31+GjEi5UUyalG/CDGGTBKgqRkYs/RHAod8QgQUbJYdvV7+ooIEcvB9jPooLD8M+lTuJMDnN+7hAEhXxQ14RVxIY41ilc5aFeuP+UHFyUdx7++uuxThLVocSEydbuKIm5tRoaXSA0VTQ5/WH2T8BGyCcTiCECgsE8xqjEXrFOCuRAeCJCfqAAjD1ZQ68MpaAnfN4OlAbgStudre3MlDbvqdPkFdUubJeec/AFE+jkVBUzIqEk0MDKb10MFw3y7YK+NBgQrxSQYkiqY1PWsl0Bq/g7NUI8QuyHwR+phGYnah1oRulv/7aqzo6WjZu3fbIE0+H/KF8Js0sqRbP5CRJkWlt/lPkPyCrCsCjE+PgAIvuaJzVS+IbNmyAGn39Q4hsZoyEpAhQyspdAWCmMUXQF9PlYwZoT4B6jMPYj4AiCUK50J9LUmxNwRwwUjb3+TXGm9EzD5uVSKoykcZihOWUraYZWTQyujExdXiBLoGbAxMJIkBFqS/pyp+6oyYtFmrOIhPJ4h61zCCPj5zzKgHMK7M+TPdAfUUyrbizpRiQhRuyDs3haAgHGSE1RPhOSrIRFlqQHFuRupWf0JspnReTLKeORWZdAjuiTYEjUUA/tyM9mWf3YK+1EvFt8KnAztCUojlE/AWDgVhkHaRkbDhlpsps8QaH+uQnP7lv3z7DTRKPP/54LZGz5ATbWgKjdXiQAIcJnI9NpWArGEzRA7jd3hxHUblz3z5WsPisYjwo+9rzCMbEvCV8XC4EaEQdyF14FkDS2RLHe+HOfaPo55gwIwYT88RFqjhqgHUhlcruhWTssZywO6vBab3q3G5rz6Odpf5XXxBhQEB+42NpFsCKKyJihoLs7stdXICIFxDZrZGVubhClKhctMLoi+nE5Bg3xiYmwQ0gRIBUaofxsfZzyn9oEMHVspYt6naUilv29MkrOITAjNph7e6nXi52IJZMcDwI5guCGYKVcYQpfjYwKkqxzzO3KWW5WCjiuQFWzrreEn2PggFIjACMfVbBx07uGNOWxCS4Nex55fXXTqbSL2zeAkWMx9AqkEtq9UFQEFccHvabYoWtaS/GSWTq93llPFVkR+QoTTyeSDMgke+ECXwUFWYDSMqdTKZpw5jZFkJa0FC0koEpupzTpsZImxskKdTDCimV4AmfWEVjYYa5DEKixhEHMRsa41R+eGxCSgVhSvjdkPQRxEcns3SV5qiMpggKz1xCJbxJE4FKOehLQly9MEdimx/m03jOROTdN17+v1//1j/+y7/tNcuuprmAeW8uB4ioQScyTAG4JUoLFCdRGZJSTNhLMslCPSZr8g43235WFnFROr/Xy1OUAVr2uZTBfsemgKGAfh1nADF0+lalXp2PgS9cffXV3/zmN2E9fB5sH/bd734XQ2gqgxyMdAJIc85RARuEXrVq1RlQ1ZNaRFncYpwGwIgVgJXlsgYJLGhsbIBGcJyW5sZsvrB7117YNpO7UaCjgOZTWJURMg3PljEQe7yL8hDBjrd6OlqB6d37+oi2d38fc4CLerpJUAUFrQdJKE+XPwTRg4rHYUAd7lawPEyzAsKvWN5w3ZLA2y5f3CqJw/E9/sZWhB7cLjPvKC9iAFwoIBUl8w7RbqpsKNxaxgpNkaCzkE9MTXCJBMyxoaEBpTSB8hwepCzmgdZu1fIlrC3Zua+fHAXQgFKHtfPAMJ1n2ZJe0VAWWA0q5GAnRJNaiWrQ67iRzqoJsMCP/DBG6x9gzW5XRxu5CClAQhH45cj0qpDODG6uuOKy9u5F7LYEK8f7Ei+CmKbIJgeZ3q78qCTiLzQr5FnfLAVnPS7NynR7iBlZl7V8iSBE/+CwqKAhOC3tYAssshI8rmyMGBQJ+IjUMPlVnmkrqQTMQJZHRdlCudKIqRSoUwoHVaC1GGQIAI+OV1IQky/ZeIvajsv62RIScH2OSMDcHBpPEEECDc1jqsdRTpinR6IWdX+e/cGoZtl6fvuup7dse3bbrhd247PEDC/03RM8QkexjpBshWIybJrgTpztxhjv+Zi5d2eYlmCjs3SG7THY/Yz7RCaIXYXDYbTx5to+2BSYKwXOGABW0NVqMvzkBIj94Ac/+POf//zOO+98+OGHP/OZzwwPD3/oQx8CmAlANbhLNKRkZGJ9cWhoSE/OniNTcMwjynaEyllNzTnFyQDCWkMUL1KCf23NzXDtoZFRv6sc9oiAaRWwqBJ+yqnwVQEhWGKJyUgBYLOfQ087b+HBYwhC79m3H0jo6urCD1KFPRu+ZjKsHoTPgiMSgVPQTlJ0IAWXXreh+39/7LZ333h+1JEEiPJld85tDWOUCgCL5ttLvJLbn2d3CWbj2NTI2M4wN0lXYB4xHgzg3zGbnCJZhmL0kChq9FqA0R4xMGFpvoCli9lv0Nc/PDY0jo4Ze6kcNd7bN0QXWtLbKfZTKi1RfOHBQLFQRNWS2ZyomDXoAFHGIpbFtpUcpcbIxoK+RbqlvM1eXgVWBhXag67rb7iBlTosp0aEpYhaGOm1BwWRgOntKIGYgtEBAD6bSK2lJZzLy784gOHxsDBvdFLedMh2SuypJ6Uk2UwO3W0ZlbgI8AI3BwdiKHnMEXrwC4XDZCeUlDeEfLzG6EBQx4k7a0mB1Jqa2DnYjcqhSgGqKrFB7AmZGy+oslrzoxbNDRHmC4ansOaWID1JJXIZTklp+fEuqWEozUnfWP43zz5b8AfC7V2/fe6F8WpJzdtzO1Srb5gDTuFqVWfcRgdmUThlChiCY/SWSUt3Am4hr+bHU7CZHpnNUkA72BQ4IQowjj8zAh8AHEExle+EE9jfW9/6VpjOn//5n6NwXrRoERrmmiKaWhFHoVrxmDtIRWdGbU9eKbWB2aGVnWrE4EUmN51w9P5EDqNfn1dYNb+WqNft9qSyWfSUmB3BAZk/hNe4i3kmvJRJGSyGDYklKnyZpy0NEVcxNzYpPiwPjEzgtLIp6pKNbgTAhdOhfAUwDN/nCq7FCl7ASGxsuIl4gTUtJwhVCDzBEOA5gvdCipXGJjjgYksmcsmhAmUhrhfOiDyM8UuJQlIFXkReF8Ni7KqpAwbJ+XIKsSnLrHaxGUwiT5H/sV0yzltEk27eMoWXZ/xHZ1uw2r1WyFnsn8rjh0SWOjnY5M8aGp9wFxI9DZILXh4FV0B9cbyFaiCPeZVs7EudWW8k98V5oXGx6RocnWB3RjxsQwbecniZQxZ0YccFMpQqFQshK8jg49bLW+/+9rDHlfUV8UTdwT4VVXwwMVVZavTnfjcbVeTQA0E6GP9YIoNJeHucLaSgVR7KxUOOPW4nbjIkBWRWl9hsG22D6JBZqI0rLMld7olVF0fTjHImRWKEJteC7zz1+4OFsivBChyKzXMajSED633RQWAXheRvuk1zEAOp0mQqmSjKPILsEmTgGsKil6LfBDyaqXyM1D8aDHkcorwVHbkphCmGFBmBlJ9mx5Fi0I/wLfrY0y+w2i3qCTz45Kb3vO1ybpI4T3lRDhzpTebalLQylqjcqMSQKhDMTY0M8DKsE00H9gTUmbQSLCmzysGQrB4PQwGXI8sq55KVYCDBFl4MNKqpYUeGIoN2pxLHGyqfUqUwh7xNKbRSWl49HhLHvlxQFDiT2hghWGUO+JwKxJx87GMf279/PwLBM888gykKiFuTlXmqbaUwzLnqsRdUA85YGVoXroE6MR8I9k1ZHgx8ixn4C1xsX6KY9YQ6O5rg3Z6pdGfYaow1YJaFIVV7R/cU/Ij9a8UJcdFRzMiSHNhlpLFgBbKs4AT8BDgLS5o8Udwq5rMvjlk7hxJIz8vbQU/DBGW6El1uHn0yZeBt+Ds72aFbtvDqZ6aQo27Bfs6FdTrY3idiRdosXxQpOQL6Kh9kQi6EMTQWRR52yUODGfcKvqtEigcNFLzZXDHexBqV6EjC3YcsPmE5JwdXxiwXy13AP1mJxMyxB//J5AV4iopb0ES4HSiEyh2Ge+HyHk+k9YEX9sguy37/zkl8PU61OSbXk45M7PpYbgT6pXHXgHRWzrF3U3cLk6GerVu3ksgw9xHT80nKtv3ASNbpX72kg+leqZ7AEh45UHJjdMYdmT2mFRqZui5YH7n1wuvWRld3uiFFNotxklDPqKiJwsvyLndElV0qeT3oACzqmPc3UIAm9AIQr+yJMosfyTubmx/fA2RIjgAle0eymotHyQzLad2+cJxqy0PRvUIEUgJNZTRk1uU60U3IMOSI/QAAiDhJREFUrr451oZZwXCs6IkOTAhQio8usYZHFiQVjwu/JaZfUeDuoBUoTI0xWYqiQr42oS59gzJMjA16LWdjqIHWJz4mG+jUu1uCmcnE+OgANuQZ8qd2DBNYuJwRzTZ5oT4p57NoREiMyy9/84eBSPxtr3vNota2iZz7kR3icVNhLzU5Idsz43kFu3pOzGCFjMiaHycmkCajI1E8iDrePGB6Q+zcRfjmT4GxBAM8LvYMD2M938gqdjO2WNPVOl5wvjRCs4ad7lBrNEhLMPwijXgUOqGdFruAakaV/Gb+Q2Q2nORnpvJJSQI3zY9LIYD8tDr6QCPZxwVKATqVHRYsBWArMFO0lyWvDz+FqDCZdYMTIXthIpOxHEHkXpi9R7aljTVEGbuwasfr8SDRisdn+CN8jf1k4QiyyFUkY5hElXMAi2IIXcznXtyeZx929KjCbWEclUBEXhe1JCUxFwLDcDstmAIz55IR2CxG0Sx5ggGKHMZTwIwLzimwsGuwQsqjbFBeE64K+6LQxPcEis7gQA6PjO6AIx/zlIKyBpX4iMuySW21XAwLRCls3pQjGYUta1F7Iyuw9o1g1OxhZhpnTehPmkNuhiHiXpmJb1NQ4f5QhvNyvru5oZzPDA2PKlk4EjKZNGZZXl8o5Lf8+nnJi2heyccEuRRZGKepLW7rjddd9Pu/e8u5K7q5g4dHk0mNwrzPDTekYwte8BElELmM462s7GK/ACCQilBLTlobfaB3/1RSUmBcwqtiVyVyXjYvpmxsw6yJm0JRfygjbUKN9GeaqIBvFqLJ9LY7kMrJU0FDaIz/S/ItZF1s68BNyFCShctxv5OtjEdSpoHI1SHDBVLIZFOo1oP+kJIdzGEMwjYMfq8vh9sRSkck/mOfj3aXZ2atGIMTnJoB9+T74t7s+Pgk3lquu+zCy85b63AHf/qr50gtlZcC+42zNkNI08dMkaiPZmeuONdOR4ubB6bZuSW7PkgXQxMgVcNvDCEFca2ynx1GGH+U8ZfiLDjciODJAu5ESj5MFk0aZM0J9dSkOc4+EFkLYkg9fVGXgklV0rbDWUEB8zGeFTU9eyuJrSzbSGWz8EM4kqziZYYzKdOl5VBYeDjiJPyxo6XRJ5Y+hXAgYDiSoRg3hNtUbvDnkB7T3dmFdhH1A+Dd1tbGO8Q1/IOIB8U96MKkPZvDdEmOHBuGxupk4bqhoOz7NDgoyk8mLAIBp1vn7XASeZS8JXGjiuTv8qXLEOX39x2YyjHicO4bxLmEEwdbPBJtN2Bm6sMrnMhwwcI+q7tczOzef4DxAddSa5dvcHyKzZXZXyokcuNMgXkTUczKTGoTBTaqcrk8nMjc1DlIlMnEmBwHYStbTSAZqzKgt6sbyapv/z6hRYH9CWWYgdaXg64IMPtS8uyQME0aHZV4zLQ6XqxJAVMviQ1WITWzMXMqD0gFAqbPmGQwBY/HG6jF0HBaSkZiom+QirMaG7V1I/4njaETFtHUy3h59KcyubEJU2yHMwW6Exuvp5mE6t8h5nhe7J9/8+iTowP7zl3avaY9eNWFa6MB7+aNGzHfYjQBCrIyKsNUMpCPxC1dVGo6+2AGgZXoKEYY3rGSAqrGozHKSR9G7kdzhhVaLoNvGrEp4b4GSEFvJ7IOuaq3j/1XyGN+hlKV+LVkzRPqoj/azyD0sVO1Y5zBFJj+/M7gSthFPzoFQAufx435MNu5u9AqYwskIhE6v2TA48SboAQwuWh1tLa4SjlnuRALi+SGiq7C0YSRHzW0tjajWt2+fTua3paWFuLxbt0LB3UwLurYzVHTPOSBviUJHf6yGRmQNU8aImEW/PT3yUQDUxWVRa/GXECknaMEM7gQMXFRd2vQ6xkdGRtLibHZrr0DxbIDm7Lae6QBMQV9CaKudS/tckd8Lky3xoxLBuZLi87AvqGJUi7T3hQDnGYOOLsAulhQRIGlClj6MFCqf6dOEgIAhOMXmAXHzBsKF2MsVjVB3+nu7kWa7du/X+6Z+UqGWzQsIMGcK/J7MFAtvL5GQ8lc/HRQTMLcmppGQmGGNSyj55wOI+MEFwDMvgSuCNhcfYmTpoY4eDU8OmaaQh5ABzoAWzIDxQ0o8KuR+Qu2hMNBpqQn2EZRGsXpDQYLlBaR3e9FDGZiIEn/8ViDOeup5zb6yuVbrr8SLXF31Fra3hoJ+B998oALv6CmVMzfmgapFaeSE9eSttFjV24d9kcra3yjyTO264RKjHLCOiQ1KylA2YmJHIZvWBH4PdKeJlkZOigAV83fD0v96DdIpFpc8xdKmQEKt0WTU6kR9arGqvuWjp6q/eRMpUC1mc/U8tvlnokC8At+OPdh+QruOTFPZh4USSSN1718FgtVcRoJvoK1ZZYCN7DtoKecb4iwsKXCa0TFJ5xV+gn/NcH6TtPc1Bjy+5OJyVKpgCTHI5gj024SndgSzB8yOTmsREshRQAYNIglNNWMRsCMvr5hzhFfKplrjKMfEdjEiKpUiAVwXNUCwu0bwSkhS6L7UeN29nTzKjCG+l4zBkIkoCcvOZiC7WpuwKBob39llhFBanffiNtR7G6LE3+GGpMRyQCriFlqO23SlYOpTe1KTkiHmVXqy1uUBcduoDUWhVJHU09ozugH+6bR4RHOUcfLE0VfbLiLeZm6rTUIz6ptI6cHB5CJJCNBF4rZybFRMpWfwdQJpno97ji7K/A+yhRmMkuyKaEaQleSNOM29rVIZ7JejyvsN9MJUhhVigPJMsc/PpmgUmK8RqXoY2JEh9xeQMhmCwjo/9BjB3bt3n/xeedsWN6WG+8Hg19xwTl014d++yjNQQQGSd5gWCRgMq7YMh9cE7k6agsoAItcbwKiPooTTDhx8Sa3ZOgQRbc/MT7KfdqIeRyNyZGhgwFgRw45/fiCyPv8Kv499V3KaDDYKN7x/EZDm1IdtezHl6Udez5ToNL/5nMR7bLNmQK0LlDa3cHa2sLevXth+WV3ALf2sBtAoikaFj7NsJv5OY/V1ohBVdZTysZDMtjnxWqY7iScwZ31Z/5abS0hL1PIzKlZpfbWZn1F5hYFGAwzlxMTKjyvenlifyvcCbBggtlY2SEBw+VlS0SnE05aS56btfMjnpTy7PrELhHW6qU9LPDctPtAAkOn4XEs/braO3gFwz6kTyJgiMTRLMrysgIKlrxicSd6hU1bdwnFXKI43XFg2Od2Lu5oggai8j9iliRi9ApIwHB2SgiyIoFhrX14dL1VlYAlPcZS2E6hLNXIpMTdBnw4eT25VHoM63aMhLiFZbm456T8DrxgMXSAENNtycuHE0aQTOTicBDXlW6kv0nWNpuYwB4OrlnfzPhM32PKlJgAMCOV6aXARHZa41P4AnHgVApjK+JQWeoouZct/GOXXe6RcWMsZjCY9HEoxnpo7odCePy22OHyV/c9SDve+MprKHbAIxZqr9gQdWUnd+7cOYRuG7Q2GWHeDQbjHrRWs8PrRMSjhioAT02JB1bGNEofWpm1ixQAUkMEvM97ZRNNgFJagzjsT0iljte1vCmlkFcKqe1aKZnQmBuMKvjJd8o1kQ6KU4lq/1lIFND+tpBqZNflIArASlYu7Ql4PFu37UgjnmJaUraGRqbgjI1s0EpcPvJ8Ae7Y2uAIuPLY1kYD4sG42jNkdq3yMwlX71dy6WpjS8AMqQHa2FTrXdiaYR0Hx50jNxHeNFMAf4yRF3PAFHtwcPAgABbGL7WcIQi4usRAetVStLilZ17cNVCwxhNZDNRammWIAisUExzMqMzCHtkyWBQDskHsqkWdvkDw+U1bNX0AmC2E2dKus6kBGdFoWY+cs87p8gxZSo9I7bKQ6SBVAQQUGkI5F/st0lBmo3vjhaMYiRhJ1DwlUoStDgIoZ4tDQxPEFIMi5jUB4GyWjRxYBEwccoIWZFEXGFTUBTG6EoV8OGBcV7ocBwZkuStT3kQbncQ1I94/aG2lCWuQWNobBbqGRyYENgylOYxNkbszRHlIjxSN6zpOsDFrboxDuoGRcUpBy3CH7IqeUM4XhnoksnVP4qH7XxjYv2f9mtWrV7aK9tsfoIl7/Nb5yzoYqdz/280KUTxh/h8zbhxlSDbHExRKKYExMxNHtnSDuAFgyk/DRcOQtzwxMZbLIW9X5uCJwzkFpo9xLqvFjzMI/aVlTNC/xhSOU1EG0MQGg1k0aMJBjaO37ONCooAM6+ywUCnAVwyz6GlvDXg9fX0DEykrEBT+OplIeBxs/CK2uAS4OtGiAdbCskQiy7Ic7nMHbmOkVmE4SiJlC5ULcxd3i458yuMoelxl9iwgAkJUFfI0ojIRjgaWK5xF05vVUWtxeFSTOrpRGJdwfhSeKPeSk5Mhh0OXq9VeEbnuqPmaZMQltdXdFMWbx/b9w6N4PcwXon5nzC+kkGDGFKTvwnxWOKYYDPEmKmhEWJyQcMm3BIRAZQyJG8Ng00wB9g0sKfoST0cJCMGy5kcxWAtMliYZAJvFd2Koa4yqeDcgfrgqQTl5yOdjkhYno/liHKUxBUACTucxIHKyjBjcPahItHm1nYQ8moSkh45UNsBAfMV6gAnufGc7leH5RDrFDg1iOW+yhSQkiItptoQam5oy7i+lJShwAgs2lyvgo18Y8DYr8qWNxMtjkDqMTSaJBm1z+bTlYW2bAM/GrQPsZrZp44vjiUw8EL/43DXkK2VmS45MCm8cN11xwbbNGx989MmbX7WGMrAFCJMlGH+xkne6+KZssz8IwJO7mYZHcUIhyZGjFJ6PQRzGIexiEX9QDxIA5rG2zewzk5i8Y+rEp1UptNHvGx+iiL88pmpQRlTe0tMIEt8OC5ICdtMuyGatVIrWRTRobmApLRzV2ts/KkIwe+mMjhdz6ZaGMIwGWcmFfQ7ffNHasGZZV1OovYHtkdhCwPBnhzPDjLFhPsqbpnuMTL8Jm1jS3hRwFZkKbsA4BXuWhMxT1oXpN+puntApjItfJRdYo5kD7GxrAyZAJibtent7pzkyUuX0xRHyZcdfdABwvbW9LLSCE/t+9eAAbLejGQdTBlbTSCZsFMtyUakL3F4w39B2TW9YdtkqFHccEPeKI0X8OYg/5I5mHDkIqY8WQNwa+tbiKPpWKlZXZtIJBMPprKhJiYyaHf/nzc1N8qLMR8pfcl+2qBeY2Luvn0KDzkRlTfbe/iHK3trUwIrZKsen9Ic2Sl501lI3UCWXluXI+BkF77fu3KOVJX28hecLkpTEJC4LiHPWsqXtSOcHBkeQlAW6TUnYapq9/Fqb4lGW9LIEm7GRbIkh7dTR2pzLF4kgyZYKQba7RKluWX/2P7712c9//b4HHy3nUucv7/nIB3/31des5xXyzeVdbtmCw7pkNSuvg0OjE89sQjUuFYdIeBCpI5UUYDYB+BPHptL95W38+dAiMeOHUkhQwqu5lwijYyOpNCrvcktTQzqXV6fiwCTKaiYFsJ+bTV51cSAycCsDQiF3pQdzJWUg2X/+yk/e/ZG/3T0mtTZdXO7bYQFT4NDvcAFX9SysGh84zBkxore7PZvP7R0AIOQ7z+Tyfo8zHhJrZ5hsCVUaKuiQ9a7fueUPP/DuJe3IwFY2nTIUY8sLhNxKPyFB/SkxOQeAV/a0Job72uJiusWvMSLLbyXUR9U7x300wsFBb8E5D+60sN9SkWLIAk7xwiXSZL3JzEFvH3wBe8ONpJvJXKOCDjGnu6iDDRk3b9uDu/3u1kbogLzrwZMV4qMKfmW0+FRMGCjloL6Lu7uRh3YfGIKwfWPM+7oQpFC+8u6cJCTlxtWC1iGly+kxez3gEjRTyOUxoiaS0oLCUJLmWAxF+dgEk8CivZDaEcMUu1RAES1pStErAVXodEPJ/j68YYYWnJJaV3sLA5oh42ZSd55PZEBNB75HeCqBDX1Z2osbE/xWlhzsAUWOZE0WqRwTHU42AyZmLUchV5k9pHHR5ZhMZbiUIYpYD1hPvDT1+Oa9JXf4xptv/siHP/jpP/29c5a0teKaxQBw3uj/cZ/S6LOWLlucLzv39Q9J7fgvNTV/5HQuoqLqHlipxetKUiohRWV1NZYRbNop7iqLgL2JSf2kVJyTr1rSScazD0aolQYyP/MeV9JTsD7rG52aLLoPjMpmFULLigQ8+9TtmGcYBehpdljIFEDowf/C2jWrs5n85m27+bCRZydx11DMxcMyaepikbDfhTsE+HN7yFrZ3cBJIYeGE38IwiaYsasSSDhF3U+ACC9GF6xe2hL2nLtyMY5zeUFu1q/P4FoY40npaZVEaoWQghlzYjKJhnHKhTNBvC46EBCrZZa/VVVr/b3KObu9Gp8MAG0ReffidSuRZ3ft63OVC2sWd4lTCZZvKYyQOXyymBeRm2xhj8b66dy1q5CYNm7ZAW3392Hi5mhpiutKnTkCMKkL0UwwE4aUgv2/sEBmDpi7oEU2l2mKIxNKoIlFHESF3t6BZnxgcIxIACBvUSTmgPOFLHZM0tgQrj5Mz0ZWxGgMkbF4Zg6buMsX9xB3f/8w+mHak1cnp2ThMkb1ovc1SUEZLpqaW3Mlx8DgkNqQke9UMltyusMGqivrZyGzAeDGqJ/t7YdHJ4lGKJilxvsGk65Qy/mXXPH6111zwerF6NabAzK2oxgQlK0cpdqlPMvVzz93PbDHsjfyN/p4AWCoVSNYpYuYxOtuV64P/8P4hJJgbMUj9pYwEaSoFDsoe3myl3aOeWai1TQW5OXEdQ2e0Y7XCkuzJzkKr61RKbfMRDOA2z04lnUGdhpNlZn1oN6HtJkmYR8XCAUO7q4LpFJ2NaoU4EPHWzKC3dIlWO1u27WPDxqmzMYvzlKpGScJGowaM50URuNkI/J0Fo8cupAW2dhYuBy5n3CXdSZXndf5u2+65bU3XB1npalZyegX97onKxzMgEi3KrdVMsC1n9F9srxFnDbDHNktIBKpPK0Tjw4vELFdPrPYFmkmkwKA1y7v9XjEwWUpm17e0+pmKrUEAAkwSQDJSdBoctF8aiXXrlpazGe27t4PA0UTC/y3NrdIXBI3FkbmzdkdFJSIWzupvScqa08Oc2GzrhcgjDfIoIbiMAzghOza8MxVKg0OjdVIRqn39g2m06me7k4jANdSRrjlJX51QXoLP6Q9GbH0dGEgVRocG0vqbeaAkynUvZFwwCk0N8FYpbV3dkKnvoFBHeiQO1I4SoVoRPYi1Jsqp9IVpZk83tGpKVNFnDxGib9zf3++7GL/KFH6Fxn/JZk6kaoVZfhYUeaX8tR66dJOABiTfim9Fl8GJaIpocy1oIlzrJGi9uiQE+wgAHIAmBI2Vq2gUbJArlgkitm2irlMU1Q3whKquYw+gV0uDkltFpdSNEpF02DBbYJYrXMX9cJQIlsORveNjgt9eVq3EFyj2scFRgHtwgusUnZ1qhTgAxbHjew2KB6hh0YmRvAsz3h/csrpKLY1x5EuUoggBoAxfg44y15nEfjEZIeegZd/uI9DzHcIpquAcPozOZAykiY884ZXXNRolK6wLZgsPwm8Bpc/BC/10fEdj8BFK7yLwog4IVcNUbNlvXB8JvNilQgmI8WAI+bJy4Kt5TKb7brLhfaGEMZHQLi7kOppxuw5p5Vh/tcwROMRyjjPQiXJu9S0p51jcXAiOY4N9vAohMMjGDgJvaq62iPmfLSbh1aWa36gLxIYIhd7A3AM+Mz2eKRhGkc/48aolIcltrhZBpxg4lTtpe3bgZY1q1ZCqmPAEYjH+5KWtBnDKWz3ptKZMVxAGyvoZBZqOFntJA5Nlb4l8QDd0d7F+qwD/YMQRMOIdCoXOwuRjgxTSkWsjfkLlAY9GDWH0tnCZJrCs0rJhy3Vtu27ybm7pRHchWges+iWvsSsBr1LEiBdNpNwu9nYjPZNTI5zk7kVjsYAS+umc9DVQshfJUz9nco54E46egEAs/GiGbdJgjpmpTzRmCxsg3pM9yIQa1rkxAk3iIi1QSW52f6BQlJU/lR+lSLIHeicRJUfCA+MyehE1Ct8v/rxzTZ9O94ZRoGjdtAzrB52cY9IAb5js/UNRlY9vb1sLb5lh/DlZCKdS6dw1oTzZcxrVZ3odWIJjCN48WKoHI9HPrP8sbo+VdigYR3mr3IiouayOIFkr1oX86PsGAA/g59I1qciGKZrmHIldVEQC2CwasSP5aoYrCIBVxjbzCUwbE6UybKsCD9M5WzYi2/FeD6fbYj4m6CaOME2NeGhpgXXNptyGQ4s/Jq9k9uamtK58o4+1ncNU3f8Z0FSHs3161I6k990AnB8NK24ghwbY7RQxgiIxyYLrpA/pXisHQoHQ1iEDY/KM+7uTyAB97e0NC3uZXClFTDH+vO624LbVNAAMRpgnHuACPv6E0RJsRtSqSg743qcbrxrkzc/45mro7sJGOzvHySakcjZ7HLK4XLGwhHaRvoSd+WFEnPGVCkSiZHOEP6jgTfL0zecmZyYwuRt9SKzYZPUHotAQW5HOeu2sryCRr3kwEc32Ewd/XhVncxIUvywfjdtw2vy5iwDU7s1lTKKZOzPoTCrxyklQSsn5RfclbXaVEQfaR6yiszlnKMK2jQW6VBHSc2IuZz3j4yw4WbO4eofkzVd8ki/TCmRHRYmBejAdljAFOArNhYlTOktX15yep7b+CJa5SxgCVwhxMHRcBplWAvesko51t+krFwaG2juiQmNYQR5GaAfIRCH6Tk2QcKFlkwcOtgckKU12N/AcM34vf4lYpuM6u/N7VxLUymTADCGsjIRyMIZ4AipZdoPB6x/xmxhfDJzKQUDJ/JRn3PJ4l5Ur73tbSCQUdjLiloWmgpOE6QWSFoibMEleZVoSxf34oxy45YhViHjdqqrK67wbF44OQeUEiSExdz4uOx2zGxlpdQCghRECsOybkYPQMaBvgnuIIs/+8ImbKdXrlwZclda02BVtUhCG7lh/pqbeiGmWAIAPT09+OPcs+8At/F/SYuCUuzGbHanMPENTLS2kplrcGiITZdoBiiTSLK7kSvIfhS1oJ3MXLLZsNcXYPdusqGfJXKOTCrtLGYjbiudYL9hkAnfGqZUuOXOJZke4G2kbxS+CJ7YKhNnoG9KmgIMBqhlCtWM+ao1ofCcaovVinDYiZlQEC/QIsuCshCwniGKPzWm3p1OcaZu8hJ9i5n4hw480lVhhyV77BtavOl4RgU9NDzCdm84VhubZHdHU33zAU5Hs88WHAXq+9uCq5xdISggxi8lGGFvWwuehrbs62cBJkatLU3NfN3CtMxGsERkP2D8HFns9Wr8AjLfRufIZgvsuyZeeiXoEaZXF0jf5WJDJBIrYnyF+EuSHE9qUFZreJKkK2yXP7AxPESwJ5DTkysWAMKQkz2c2LK+YFwxmhKUUGpKZH5EN8WqcFkSgZvzM7VCY8tzN5vBru+KdXhSixtxSqgsXCAFSUgkYfkhb6GgF/8V7IzAu0TD1xiXOw/sG5+cgCk3RyubOB1l3GIKdsSDCEP6m36shWcqFEk34/QO41zD6WY1LYkb/lzElSaV5FViRmPBktuNDlMwGf3ztj1uh281C3kpuBKRI0HPDzkV8Rczarfsf2sGFp3NUcye+8W3lsX8L1oApEYoYShoZPyyE7J3+NjBPjuYTo0YT+Okmsrl8U8ma5e5kDkLBmTsNiEgzC/odwNpYwnw2mJOpG90AvcvbWEXq+KCRvmczeVzIL+UU/Y98LulLbikUlQkHvQxlKRUFZQimkiKlKuiHeFCXpUD+3f5WAYlmgyjlEAPzlOa0ZiGyVvcZjtLAkulhUrmLQjLDxsyF67RkOWNow+5yZq9ojQ6qEy/Z2fDSkdnXwb9GfUyN/keqB0Gj/w44bLS90xnNLlU9f0Q3PJSlKlkOuDGGV0+k2aQLKOQOvtHKZYdFh4F6Et2WLgUgAlhmFRMRyzrvKWNvkj06R17Xxy2cv4gZkqwG+G0LjccpbI5qxOzT1FL40wYFsPbQZ+bX7WXwBGqP4UtKCcGsy5fkBzYrtkvrAp0EoYlbEq4YO03NzLzumjR5ScgIxAopl5sbYw+EwVm2Rlg9z822CXLla2eoGsqEsKZv8ExxhesaGVIkJUJSALqd3bGLcL6uMb9JO4s4LPUgKJ6Qyyc4cWbVsf+6+2v+NN3XAu0WN6w5Y4RmSgiglIniOX0menVst8t8FNKWRetWxOIen9x/y9SxezSpUsZjQh4i1yeMn+P5wDOK8X0JXNFzl5HyuN1DBS9OxLsseyIBr1B3eCwkA76EfytKDWwrM7ueM7t2TUyBd+H6T/46HM+p+/Va9e0Yt3MfoJQjCpo+gauOKXK/ISw3KEDkIzHxx1nIrdhRTcWyxu394F8+WyxkEp0tjVhEV20vCx4RtAFkd15ixng9d3hEbf3scE0mRJ5Kp2eSGV6ehqkHVhu6/KwZEjU1mVZMNbTHE5NjY5niiTBuOmhJ59rDjsuPwfFg/QdyoI2hY015MwRtFwRoIhHECHMCIPlzu2xyYLnpYEUeYFb0ipu2ewZOJSrCojyRx4aOjAETWPtQB15BTgE3swGSCx+duKDdHB4HK+Sa5b0UnIe8dBRytOCbY0BkJ/FV+ylLGQTfzWlsLhEs8KxaKpYHk+bpcTSJ/NsBW2VMvmUDFbIIg0R0CqXrEGsxzlJCllY02TGQR6Ti5neljkOX1Z2wLQGDgyz9NiVSeEbdTJrqiZbc5KbHRYsBZRRLNjq2RXj+wWBRW5gO3skEr/nyY2pTKHAtqzKrtAXC6OSC/5zelCXOOzr15fq6Xr4nfqnJ+UcDidlrNhzcWUkGKBK1qViH4RUxp0yOthU2JOPhRF6JCBDOQ1KO0RqkWlFkwokYcrXiGWgUqHoQl7GWTYs3Gw5t6LF3x1u9xvdddmB/ylJvPKf90nFKAzRRhrbNQtvWWxjkUpNtbQ1RzzBkBgJyxCBaH6R53jneElkamiqYHBR3pdFzqUii0RDebx2ucIh3W0CsVcaGFIwp4BU19ocTxfL+yeSYMBTmyb9vmhXS7wzLB4fBRqNcS9IZuqjGVRPpYYyugKpcfJEpSNeqznic7hduBaZYvusbJEsQj4vwAjVsQ6WKpZlfy2wvz3iK3h9e6eYKQ7guIXlUl4m5IEPnE4YyZfIUA4/J+xr3BwN4Ep5PMUWRAJLI5NJK59ubzQgh2aXZIVgoj4AepUW3OA+XZOT1ngErMUYjXdrgWjVxq3dk7ESNyUF/lc3D+QOVMsXcqzMooQZhzU6JSIngj+tJWQQOrD0yYoZe280Qz6fUNsUhWYvsoEGSng8YGbLJVmLzDOs5NEeFYoe/KWYoc9kwRqcRLgvjI6M49nEyoxff+m6riiyeKU/SEZGaC6auW2IMzWZ9LG8ia/V7RkatEo9xNAaS5nssCApcBC3XZA1PNsr5WDBEQKbBXtrb20Z2DvxwjNPymRnpNHwGtiB8BYjeJxJpDI8mjnRkux1rHrFMktcwpnEZDwkApzwOWWOCJE1GX66inByolQ4Ln/BCZ0I5JzJPy7loUFfTg4KopMmiFQN8AFLsSCo0LBnbGpibKpjzUqWZvEglUpHgrrX+0Fvz+2CGUrUpIVcLjE5xXijgXlQMsHHMmgBVJkCcWdxd2+2UNw7iGbX+u3jz7D8d93yJeIGDUVrHo8iRDly0LlznnGi5+QY81oRfzCTTvcNINRmmIfGx4hJAtKSJTbMQguGM+3t7c5tw/v2HSita5pMyNbT4SD2zvXkFc018xrca21twfpqZHiMi5RlDQ0NU5EutlMU6JN5bjOtL1Xj0jQSTyqBZsHGrVh8fGJighYCOL2ArMF4UzCNbk4l8xLzEzLgcJoegetpA/mSSwktjbHeclh9mIz5wyF8XRo5u5TPE53E8X4lg1eXJ2RMEaUCEqTrqQEXs++MbDKFst8TnJyYiMTikB3t/1Mb9/+/L30F/2lObwCbuFIh73UUgwH3665aExJJXmhniih9TMtKuUcnJqE8jkoYCO7vHyn1NE2TT/K1wwKkgLb+AqyYXaUKBcThsMAAv/Vr1jiK+fHhQXYCboxFDRfgILzmTAJgrQzFlqnZavnRozqtRT1dnlKho7kBBITjyjNRmMIIDRM2zI4eX+n08lgAx/yRhSXALSxVcI7FVyboo4OOvIzSwICPTpOTDGx9zbIlrnzGVcquMP4ruMmias3hoNeP96KaBMUjOxGCJ8dh06yzIiVBP9EAyPSvyEolVvKIm4u+cTaSt7bs3ufIZS46Z6WkAZqZYlfqflgxSFrvyexmlSYoTpobG5jg3z+YnMrkIInfz2S0EoAM0eRX0uvo6GC2dPeO3VBzKpUltUg4pAnyFieapJ43x6PlYmFwZJSX8Z81Njnh8wfa2mg0CUWjOjan0+BLTP1xv6OtFfPjoVHB7wommoVhJhvAGOSuBQBYKCPiKT8HNtVySQS/N4aL6URygkSG0mlHKNIQDmmfyWIVjVpBNm5C7Zz2lEuVzaWlrijxxW24OB53OBix8TodZyJjeWPx0ZKMJ7794we+8Z3vTiaSTY3xNauWXXzBuvXrz5lKZR55bksCOsiMr4BwXSGltCA3lmusTehoZ/+J0pjZLWrGFpO37HCmU0B6gx0WLgVEXYjTANgi3/x5azu/9r2EOxBGzdUcjwk/EasfRGRRYM7jcAizMiU19+B9VA2OzQCCrrxq6dJrLr3ogtUrOJeaEUQHiQ9nIsnc3aEJGdFZqw4/lbUlswkyJSypswJVwMLMdK9duujRxx+LBCPLultBP9xEsnG9mXSeTYrHjoMMz2ojapucmkK+jIVl2wDBH/SwTMM70C67fAUr5kWWiw3nrMe2W4PjCQZZF65oIGKplGWKfoZsaqBbD8BQBmvwvsGJvpExeghxWH+sdEWY5KTWbdpaW1HW9/f3o0rFzzO2S7GIADB2am4jz2rW2GVxEguhgsmPT4kV1cCQhcFV65Ie3J+SID+IyoEGM0fUyATVceiZs6FBFmqPjorDRlk4LKAo2mgTk7fkRArJ4KBc8poejh2W04nBNYr5ktsKSJ9BYY+ZmK/ArbFMFrPEkFcsAHiXUmkpsJLgS3G7Mj7ZecEUhyNaB/b+YqW8w4EjDuKPpzLeoJ8Rz3DGuuPbdz/7/HOU7Y/+8MNrV/Xg6gZknUxYbNfx3PYDk8wEmeEab0mCpsacUNoUaxOKLOXLLV+2eN/AE8l0hmKggT8cquU9OywUCtAT7LCgKYAfXYEnQYpFLVbY5/KVC/i7bzI7MZhpLZGghGFpqABX9fLl/3v0LoqAYMBP9aEwst72+Nvf/PoL13TAScvqKZjaYz3FMhVZO3QwAJtrTLipIjIHYea6GogSC29hwWIUhicspD7h+GSybuXi1Ej/2MBe1NH4iDT2z8dI8OjZmUQPfiwScJlth3DdOIE5tHh9rgQmOqkCVr5p/I9xt7GppeD2/vzBzROp/IquzhgrnEFpp+znY0pVfe/gvyqb6j3OqSwEoW5LezppgD2DQyPJFDfDfjEdkDqr8VaVpi2NISx4M6nsREl2OiKvaFDwXqjKKEE09QqQcowELcYTqYzs87dj1z7UuT2LFhHZIF9Fuys5iI5DaGiOnMiPtsZBZDgaSSRTE1PVfis7PUijGCSukZ2sqLPIxOCzSLUyWinSN6BXIUeh2NI5BEAOs+dgsdAYjUI9IkFqMgXzqUDYZ/nKuRDLy6U8BBmGEId9oqCSOuJgH0wSYcn1P33p+48+vzlTKv/Bhz+0ekVPa9hCqGcYwokvGDgwntg/ISUw36OUwXyU/JECTyRRpzvYNXJpLwr24sjEBNQgcq0yxLHDwqMAfdQOC5oCCCnFImwDKTiMjLi401lIeR2FeCgAC1C2eyZ85IaZVtm9NpgBCDnFEwOPqQ6q4I6wI0K9SnhcqjBcIwvDzUSW4ldNQ6+mmx50gfHVwvQDc2bQ15zJdLJY4pjt47BWErNisu6NWpevX7dh5XLEJrJAimPq9JBEZnfJ2wcFVR1jNFcuFAtZMwfsdDbEpC4SZCoTwGCikexKCPx4z2Azik3bd+EU+5WXXYK4hwMPQBAkMmtv9LVDjzqpWasmHQMpU+rV0eIsFXf1Dw4hx4Gdxn+46TCmXBTWjIFwmxXCGNvtPTBYGp5MQiQuTR4UE1LJfDlrtblDmhEchvi9rNweyVhbt21H8bB48WITWUhn9rqo4p28TG7aQ7kJ+KHKMB5CivgbScotaA3gV6JVItPe/IwQTN+XEYMin9xD+JUGlAiQ7LldOLHua4z4Vi7podtQK6MIEcMCMmoIuMOuUojtSMiokqC0OGMd9CBowqkOASOLf/vW3S/u2NfY3v2ud77nmot7281SNEhAn+TX29nmDTc8u0WWh5EOKZgXKS1XUruRSVbnl9pb4q2NYdpif98AxTNlNhnYhwVKAXqCHRY0BcqOnGypXsKOE0awanGXK5+K+hwB4ybRcDdBi0qYPqveeZn/ztQ/RUSD91ZhlbXHcEx+gBCjDVmpy255+BWBwSEEmzoavKhCl6lajr0KMLoBbA4OR603HBreKWwTvi4BRkmymD1/6mPv/es/ez8bJBPALqZLAZ6jpjP7B6TOIIP9lstFXHRlsingBlMouS3yJYXBgTe+VdIiURWteCzkcTq49vv9l13QnWd+UrBGaHXMYEg6HY+Ktob9zmJuYHxiNM0cMFbQovMVz1sayN0UD7JjIM0oYRBLtAQ7HTlYoyVPZJVaxVWIyovcRChkKXDJ4R5P4kG6H8K3NjcSjaY0iZHB0enGIiwUufE4+oepJIpbLK5rpaHk0jBc8wO9qqArvYgHORatIbhLFHy2yNPRovXEpi2To/3dLeHF7SYST0WtIYWn6HwpQXchbIzXRIUCfJuJDLMaGkcc5USGpecWa4927O3DGutd73rL1RtkUTgEyWTKADCV5djb1RyNNjz1/CYypcy8op8eKUp5RG+fx3quJR4H7FFvj4yMmWLqQ4lghwVJAemadliwFJDBOpaYoXIOFaUwuHXLeoqJsfzU+JLu1lIhI9KD6NPmNwGkFnUBOYWfwSQgQQMx8BYIX4PZscmijzMYHRJfwAgvEklYKtHkSSVIIl52nZ/F1C8SIUHkI8ndwD5TjNwxEjDcFicSMbI2nNewV9JGOcyTOQZRdNYFNMJBf2DXzp1Bn78hKit2xP9VmblU4eVOh8tvtnzmpVVLuvLJMfaH6GhpwazIy8JVxD1qyiS44fV1qR56ChZKNauBXFa1AVzZvMP1wvYdAb87GsDsV+zdyF3iEYOZ1QyLadH/t2fTmV37B1nVk8+mu9qaec64gSPJ6lEMjM1LDdGgxxd8cVtyYGAg6Pc2N4twTiSpVz4rbSVzJkcIaP1Z/xSPRWk1XEdBA1lFZsoiC44zjLcE4aAKauEkS8AdOLjgiuDEp3WODlJFd+7uGCp847s/ago53vfWm8mdr0EeVocqFGl5T9vAri0tMVEXmQ050PLTxbC3D6G1zmazDXhOZxZ53Eqk2TXLtbxNVPMhU5FGv8NrZd35yYBlXXXhuenU1OYXX6LAZndpK59KSqH4ME2rHBgYw6lJa2Ost8PKJKawJEtXaiWx7LBQKUDnscPCpYBhjkwdssSVuTvgoSXiX9LR3BwLySYzlXW11eobVKtezI+/AIZixtGRAy4pP5DR/JBEZQ0u7FteMQIifVwj1aLxhKfVm8dXVX2LMYEZFoASmhJcmR/MVyFXMidUo+nVLI+8ezj4KIYhqSOeqSmyAUoH6CN5iUa1WEplGEss6/DF3bmQs3jNFRdLYViEJrPHJkkKfzwByiHPtzdGEX4LuBgt5sMItoZyKPjFvo2A81HxIm51tzYD8ayfmUjnkeEiZg7YYBnDA56jEnCrERYpxMORdL7w0rZdTLg2xhtQFihdOZqdiU3Khx5M6dme2cEGiI04ph4Zk4lSqb7DmWZDX/Qcfr94sUjLBkkPv7D/c//2g017Jr0Rz2QGd5OlVMEBKsvUALZrDms8b91194Nt7V3rl/au64iQOpO/Mv/rkC8hi9/tvNUaCyxuZxti0hMsV6tvKiNUEEG9AuaTUyKHt8TxC14BYE4YpWCJ4HQW8LAZ8znbmhqIvnes0kWEJHQPvF8bDfkU4rPTzXeJfxVWcDESSIjjEDsscAqYfrTA63hWV4/VnGIIw6fMTgmsE+30vvmWm976+pu97rIRToQ48lCJZFjc/KeXKTAqYGN3I2ilP1PwSmXMHRFStTb087quThzhsZWos6/vwS8IPzaysOgmoS0zi7BdgZoKgh6c6eyyIYtDggwsSBxkcJRQ63IeDQtaiPcRzMtYh5rBxord8jygC6HJa/mSQ86p4YvXtQMhUm83LrK1XIekfYxLXmVUsbSnnXVFWAMX8pmmaICbwKpIncbnKL4ocFxF9VfiQ7tc2rWvf2Q8iRzdEJMVwyJ6E0SpW2kKLKIYIXXgUatQfmHLNrfT1dPRiqtqItOiHI0zLFqnErhnbvOEH3nLtGt3ZxcTB6ivSVaj4vdDHmZzTm/QF3BR75/d+9B9jz/7f7/+bVTwATylWW6/G29ZlsxEO62sw3r42f4nnt3s84be+KrrEFKpglidiQguUVhoFPFYN13/it9/923XX3O+JA7B5Y+Q0md8VApIA5WyDeVwPl9c3IVTTrEO97F/AxGRyBkI8lYpHw95lvV2s75t8zYpM6/JWIRHMnstRRoen8T5ZnNjFOm5pTHKGraxMTNnbvK0DwuVAqZPL9TK2fUSdsZnDscolTOpQsaCc99w+fIbLl/LwFt4uaz5EOZoOM98pdfBhTv4ypRZb8HsKkMNc1PZpWHflVf4c4SXj6/WFZELoooELEKgSdJkTe6sujaIcGL5HOGrdLMDEdbXCGelQjwWkUKbPAAAnFUZo2x28wlzL+62rlrbe+maxUuaKSWaYoZfbq8LxbzM3VaoMrtK028AWpAjU8wXnE7U2o0RSUfGBOQkohv5ZwERYi7u8EGOwZGJsUTKUS5GgwfVoiIqMudREucV+Lgoll0jE1OlcrGro51cSIHpdJkZ1XaslJBE5FeFYYov+Nfa6kSaHBgeIZZpAlQDHhw9mnLhpdTaPWZt3LE/5w0+t3PP9+/ZOMZWTiZBSh2MhSdz5XHLuveRJ7BoPnfVmnN6u7wWcqpMKRBUCQ8Yk1FzMLBuxeLKjDtPJYIAMdsBs/YLJ9Jc0CV27x8s5ItLuztFC1JgIwrwHaTmICZgYDr7Kq5Y1MP6sY0v7aAkxosY8xd8nNROfHQOjoyDty3xGJlyhEpDQ0NSGjssaAoc9JEs6Jqe3ZWDM3k9IgUYpSKtzha2wumqQbjEGRmoQv0Ptqe/CteGOfIz07bV6lUicKkvVu/P4q++QIIaFBW4KcGgr2CISfak09PrcpVwFIyMWSg2NsSpHkKSqSpgAMMHYcR9Itl3R6yP/u7rP/KOW8JofZFXeZhisat86bVlu6bExz4AIDjOXtzZniuxMbJYm7NdIzdF+CZzyMoUOOBoiePGpiBbKfgw5U3nGSoUggiVCmnAIlvqqjKCAYrs+MFS4CBDAwdz5KViV3sLr0uyUkZDzgpcg/IyxKn9tMTkF4li5uaanBKjYp7KIjNkSZfbGQiOTiW5+dSmXcm8I9LS421o+v6v7t0zIlPCeYiUESQu+xx3P7L7+e07l3R1vf11V5mtl8rJqQQFI7jwjm5Kns2l3FY+FnDn0iKMUkICk/EU02uWIWWR4s18894DQxCjt6udFHwl/DhLHylXPKvj45JhmbWoq4N565179mGxxfp1SYtqmprydGhsnBSa2IEYSjZGISC7RUkcOyxoCkiXt8PCpoBRW1LFEqZJYC5feykH34DJiAjDOJxvXpnLGUkHqkLQY/2JMGVYMxwcBit8/KQE0oFR6uSuoSA58xHpqY5pakWZW4ZH/iTRQANrCHpmEXCEpMX+GvGTtcGSDypoL9ACzLjL+Y6Ic2mjzkZTdYorE8XgoCmnxJ5toG4Fiw0YsIqihgG/7EpE+aTHmJGG2bCDpEWrzKPWpma8ZwsWsiMkFgd1QYVLedOIy6GAv4w9lQs3y772trj2QINKlFKAvfYq1/zIzZwgUFIQi02rwuEwNlC4kTb3RSDH+RSlC0ViDDcefer5kjd02TWvXHnehp39I/c+tlX08zlx3A2V9kyV7vr1QyOJ9DVXXNoZsWTBmrQi9syFYi5jjOvlMwlKdQuoHiIBiSIZC9kRbEu4a8YMO5XDzl68Xx0YGnN5Al1tTaIbMBZklAcDcKEK4Mt+Skylt1lNDfGh0an9AyYhkpIUpbmp3WQCudnZ3BDioiXeQD1HRkYq8ew/C5cCR/7aF259z8qaoSLkW8+wWWupnBdhxZiyKj+pJwh3Dr9ZH+FlOaeLHtpLTSkPK61IWtUS4ivfoC981agl5VI0gvKnGudk/K0rhCrARYNKDiedjmqprQZK7GRFVWRchRRsuLjY8pgddRBVrQI+Hab8Rf7C2AtQxRvwUWuZsWXvneMtmtNqabA8Xi8gEgkFwB98W1Tag3oK8OGZpAjocLO3p0v8b+LfwuvGIfY0pWuAKl2RAUEp3hhj1RI6Yyy625vlXX5UqhJExJagZORYR9IiqEhtG+Nx1N+Dw1P6lMisvCJt3hyesrbu3O2PNFx6TdvNb3pF19KV3/rJfT++dyyOAxDLuu+xp//vV765e3Ty3AsvueqiNkYYvJJNZqIRf7GUdHkR0Su543pb9lEo5VmrZoYvUiRdOMCMAJpq/DwD5+Npi2nvQDAka6m4hhZ8Z+wCJcIxi8cAYFTsVnPIamvrSGZzO3bvk4RYG60OMi1nkm24cwW2Wo6HZTTQ0tjI1Pj42JhEs8OCpgDd3g4LnQLwE6aadEkMy0eEA8Lb5K6ZgpJL0w90Rmr+UUOKe1DQ0prbqtk0RwEkw8UrnRpmL9cAgt6WJKbPuCBeJepBqc94QQIS+GNeZTTDz5xCYUNAkYCOP12TKsMFxEeRIE0O5l71zE27iaUVENLglQWmJpLTCKUGoBwWrhn9goToPIvOYinOxKPlnMKaFtlPlvY4PWZVbiXZWf7BsthtNTtzjYVkxOsqCqAbdQJFJHmpqEfmlo13i66msFXMgkxelgQb+GQIILQCkXJiPEV8nFNiMxaNhKM+lzefagm6IyYl6sUoyYxejG8vmRyVKso7VRqYKxeIi3QdD7NhVXlsYpKnpOwu51oaYpjBpS3r6RcPsL0RBsyNrCMKW9dfeRW7Kdxz3y9e2pP6n5/7t6/ccecTTz7Z0RB675uubfNaYZesXfKFI+iGHGwvaRqY+QT+yg7ZjGwcLl916zAKoACMKoLSon3AwoutH5HFwz4PnrRwHC31NubRnLFmmRVc9EFSo8laogH25do/MiUjIyjp8rIFF/VLJDLuYtrvKhKNOuPElDFAMiPr94nJ4ONoP0OQQw5kW4lvHkgp5EfBJBWOZmAgwwO5UY1jii0Dnhl+xD3kqd7RRKq5HlLW6kP77xEpoD38iI9O7U30aWrDz0ktJywbOdcF+7JwwIR01aNQbVIkxZIDE/Rd9kXRS471qdVuntUn8k3DojyWP8hZOORjARIfPRpLNiLlAc+5lCG6iWeO84ZgFK4SqEPlZwBVcKZ2R05khGF+MGPeEizGGQZYZOyDTVRJjJfMU3NGVH7cmm0gA01QsjMlgHRCRBJhWQlLgmQjZOQaMq1mNF2FWeWiDNfYIhn7KTGhws+zKDJLnkix6MlPTa3vagC0crIXrSxm4SdGQU6r2SMrUGVP30CMtbomZ080ggIZIVjrrUSbVUmIBCCkMRguWTf0xhYl9i9uiPk8TlbVwrzZx5fd+5jNLFkB2TUZ47Bkav2yZh+rbty4f3ahB+bDZr9I+VvOu4EqsclGGvSNpCwqUBw90G5NXLyszZEqSLFxWi7N6Jb1zdIfpWmoglaQayGp0DlEeaB5VywQ9Id2suEEcUg8O8qOv2SQtKwf3/8UVsbvuumq1oLVZFmvvbDnqtVt+3Y/84m//5tfbNxY8gff8ebXf/VT77qi1QKxqQBfBquSMhb78rKxMngpC52lW6jwKrtLm+/EMCq32RkpGvUxA4AxM/V7evPecNCxpE3WIPlQCYBARTaekuoL1gGxMpAolFPWTVedX/K4n35pN4rrZMENF5soWKmCMzE14cuOrl/USvnHMtaSHmzC8nv6h7kkBbIgqdqPy9qdAkoAKSePxTjAxJIjehDRw8jPkES2Qy6lh8dk+XYpX5waLudxLIrYTRQSJk79rz6HWrZzOJGiSRnscHQKCCt+WQIjSM2X0SEQCxjz3eK7h7Ek00JsR8M5EcDaYDAIrBK/ubkZhMaQgTtTU1PsGccr4DQ7w4DZpMMjjcnrsmzDDhUS86dCbc6qiMCdyk3h4JUwHa165+X+Wy3ukcqhpRUsrNTl4MgGgw97bzrOcVeWVwVrCSYRc6npmzJUU56mZ/XOYYU48g2YHMEcEcGErcq1nAjygw1sRIuYFvXgjrFYdmMgJV+RZGIyqvR4OP50vofU8ZBLSX6GQMYAAHbPqxr8LwzsdixqYZ9hfG3CoQlkIkrwooM1OVziNrk57HFg5pTLNLXHDF6K3ZbEQvCSSQBmWItldj4KgnZWV0N4YM/W5R2NbDxMyXFrwXYS0o4ipk9/vNNVUUBB1jVempEmUeWw9aFQBxFcJpDLoP7uCSuRKXucrsUt4Wa3NZWwFsWsN7zq8t27n53IJi6//OpXXHLxhiUdYeoFdamJqUb1FOFeDKZMoTmIwly7Fo1gSkJEfiyzYnQgpgDg/cjkuFXIdjXHTZV5QcZpgoyqkZEhLvQpxoPunmYp6shEYnDKao3IrtVwKSrMdC/+ukN+aU2f3/IX8JriYKdFEqecBFnaLvWTaRTeogRyxRYT3EKA4SkW8uI5lCDFk7/6yjSCOwMNcQuNuqfoCjMcc2WzLE8jnjTMIcHUtP7eId2mkrTJi2jkSHYmziFpHZZQfaL2ORSY7uinnxygJkgJ3AKlmjvwyaUqefQOmKqgq5cKq7zINBh3FJ5r73Ji465Swz6e4RQQlq2s35zIrCdfh2wF73EFcdbAM5dscnWqA+Xgd+GG8/fv3XnV5ZcwQ0ne9XyVr5Ugw1+vt6kRV1mudCHV274EzgJjFq6MqFWo7LclMQ0+cOO81Uu2WomlSxbrN6v6MKIbfe1RqyXbK7nFbBnbafS3qHMZIhTY5I8RiljbWc9t7M9MjS3uaG5rEcupQroQCbvPXxH/ww+8O1HM9PR2dQZ9EFMYnwEOASpTJMMK9daRcxc9MoHqgL6i8hCj82TJ6h8YwisW7qx5Ig+YInc70LObmV+hFco+MdE208Dg9MjY6JY9I83nNBEdzIZEA0ODcK5wJMa5iNFs2BRwTibzWJW16QbC6I3F5g7QxwxAiIq4i00legeRccmY6QVjj64kB1S1sKbEFNebmsoGw4GCO2D8hoHszkw643f6KSRTG3WR5Q3JoC7wfvWKE+h9pOiH3JMXlJi1d6tp2H/rKGB6Xd31aTtVSVe3GQdH6c0q+PIdqmirEQBgioSYiwSsMi6X+okmEgksIbnEm11bWxsnCtX1gM1NO9gUODMpgNAlzAt1NJyMzwAlI1w4EPDBL4Xxmt2NqgZDp6SKMFXhq2VrRW/j2978xkWdjVwhbYqu2LBpeSp+o0RXy42g22oMufcMjuD4Sb5bym22I2LUIH65Wd/j8YI6CGj40nzf268aH7uqNUY8NKMsM65wakBabh0lYLoAzwJoO1sa8B21b2gC5WnJ5bdK2VLZnXVZm7ZszyUnrn715Uaattqb3DnIVrY2rOgUQ2gDvaRQGbxoVmVR8FMqrkwhKiU5pAgCwMQwxSMyLIuqoxLoHx6Ba3X3tJjVvZAGyVjE0WpqhmTGEpp8L1+/5se/uufZrdsuOKeJ3RExjyOMTk4UHM5IvJlkiQPpQmHv2FRubAKrbqMBEZSVVdI8RP7nL+0uV1AC7QKElplmC8svHIQggBulCU+NgZlJ0hf1JfCF7WPPJea8M2y7FItGRFMtL1fqW6s2NzXoHSWSyY7b3Ks9r8ar/dVIvMCJHmuP7JMjUaBG8yM9PJX3FFmRZenHqJRlOEnnM7NHnNChGe9zotO9qKOBal5B8zw5OclTjcyRz7WGvnqfaPouT+1gU+BMpICBXrF1qmd16EPLxRx8kxrRw3kkHP9UBrgDeOA3mNfb2cglv2JWzKlg+8pszZdbUWNyf3lna4O3GK2uOFc+LPCAulOgQSRg8ZuZZ+rY6orLbCuhNrZWYXqGOqkAiJjYFPWX3P6hRC5BgmIS4Mk5fGyEvG3nHlcxfd1lKwBK4SlGpgzjE1u4SgZFPpDHuSGtkND84CcUjCHNUQlaEX95rw6Ayw4XQur45CQ8Jx7XJybhMjpwyZ3KkqaYVVNDKFa0Nqxe4nMWN+7ak6IYePMwGU6kEgxAGuKNoko3RI43BFjQNTwuDj2khDLXzIQtJ/zECl0GXrwrPx5JLE4BVLMcrPbIpG7hIs0nzkE8smIq7bJ8kaaCKAxYFc3opZpMlSIQpZJw9Q5xJGhiQqSDf/q0/mgKXekf9fft88MoYLr/YXdPww0YCHipo90XXnjhve9974oVK6LR6OWXX/4Xf/EXfNXIwQjBY2NjQC+9VwNbrSHvgse8y/0f/ehHa9asYey8fPnyX/7yl9wnPoWfeRB9GmpnZ2FTYA4UqH2NhgkKqhmmJ7fhaW64dSHfJItEqz1cjLVObYC9AyT8Srkirio4we8WhWF5DcXgB7YgdKHy1FHvjVdfetMrLl6/HDsimTwmghlI84ZGlyqxJ5Ezj98Qa2oszZu8yCfMJ89nq+czVImE+HnKuYag5fGHkiVP/5isfracAbxVP7N5qn9kZHFnU0dIhENCKY3xFwt8S0yyRl3YNLNvBNs9CGrLT6Z2FVgE1Ux1jpo5+RIoosZQmWH/QCGRyjTGGkIMMLRwog4QhbMmKJIwaIfumD1C8BfWHIwHfXuGRwagA9uHmAnckYnJosPZ0BSGJhCHV1qaI6QxNDxOIlITyZtEdHa/JLoRLQ2CtitgoQBw+CgWV9P9QapFBVlm5WFqHNsuNiXtz1gPPdufhobuABtrsX+JJqMFl6xMsTnqnRpNJBr/lVTTseTMDidCAdO+J5LAXN9VYZdPDhvmnTt3dnZ2/t3f/d2mTZs+/vGPf+UrX/m93/s9PkWU0hxRKT/++OPIvkDv5s2bwel9+2QT79/+9revf/3r3/nOd27fvv222257y1ve8uKLL7JPWX+/bHA213LZ79kUmBcUMPgg/BSmp3zQD0QVsq1NMThhRcMpfPEUBpJ3FHNgCb+Amy0sAV91W4FDK0om4qwWQRSiYKhlXbS685ZXXnHeylYA2EQRtVahUCzm8vwQQkEFj7sUC7LPrtUeZ5NMeIBMIVMNRtU608SdGWplZlnzbPQUwiGWJ7hvtAQA55jULFm/feJZ9hG6/spLgDpkUBJ1sojK7cylklgvGVCEogV2Lq6kT776k8LWajND5tWhDymjM3c6d+3eS83aWpupr6EJyUAKMQkjO2o3nVa54KfKUbaNah7P5DftRPgW9EcIHcYlp9MZbxSERlBm74eWxlDJ6e7vHxW6EJw+yxUU+3ZZuUAQOZjUmQXPOT1sQzGQKKBhJrNKfnIm6VOKnOUmC4/fGkxbX/jag1/8+vd+eN9TEzm2fmDVFF1KRlfawTjhp1TQOxWKaLqVpElTNQUapSoNm8w41OLqSfW2/ffIFICIL09g1kczxob5lltu+cu//EsQtL29/fbbb//IRz5yzz336EeIEIxYjKYa6ba1tXXJkiWcd3d38/S///f//qY3vYkXm5qa/vqv/xpRmDuorEnk5amSnatNgROmQP0HWeV4co//PrfDWSrEArI6QC1xTji3YyeAS41UcqKUE4fPfrFeYhK2pCpVXq5HShGFYfSW1RPHkleMa7PpHDPGgCuSp8vnxz2HLJtCjZ5OYDqUGBsGJIwzy0qlSa2GxDOVDHguFwEieILT6x9LyHpZMJht/rbu2IXm7MqL12czObb/wqFjIZ0C5bwYNRl8RGBEG25QBnye/tXB0Ew51z/Toh7oH0CR19gQUeiSJjPTsVTNzCtzo8SK3yJWZ9iLl62Y1+pqieMvZdvu/aiv0TlT8olUnrFMOCTaBRCM5V3hEIp/19hUyvQBZ8HhyTjc/HIIwdIXZByD25EUWxFb1r4U+yom+kcS1Q5DIeCuXGGVJuIvv9GM9ZtHh+5/6LdDk6lnt+x041jF4y6meFtA9/AfReUnMK9zzAr5DAjlZj0ZaufSiPVPpmXxWhT75DAKVLr+YfdP5g1AVD9UZFnONWnGxQqryWSSE1CW+yq57tixo7e3l3PEX+4z6fv2t7992bJlF1xwwXe+8x00z6RD73/44Yevv/56Rs2hEPom68Ybb0QLzXRyDdprcjBxNNPaHb20jzYF5iEF6r9JOBqAp4JIEEPXYjYSdAtnxCAL1KlneKekJsJ/g6GgE22pMmL21BKUgLuCrBW81OJx5AEQrT+MhSIBXCZLbQxWGbTjgkXJsnNRKRzjs5UPU5GME7UL4QTmwPGIASCT+KBPkT2ROvi08UIFhlGgO77z053bt2049xy0wTGzTS+MhQ0qRCSVsUHtx9JfdLkiv8qPzRSha8VfKcOGevIfVASzH/D0mANWw2zo5pe2BMKhlcuWMh9vqorPExUtgFS8zpm2c3hl02Ie02p563WvfiXF/tW99wXxO+mwJvPWJJZR5VKzLnETnX+hpbmBUvUNDOZoASaD89Y47iot66UDSe5LsVDXO6wx/HBZ1vfue+k/ffYLP/j5PRABgtIQlnjlRNVeSJVEwqZAD/5m97e/dWe8qckXCD7x7MYpUBmLLdlDuuQo5OVH+5aKjLR4zbQ1L/GThjY/6XMKsHBT8SJi4LbItLM2P/kdTDrGP3LPDjNSwLTljDFO8CGIiyaZbwYM5gPjnARBVlTKQCnnfGx6QqcEILds2XLXXXfdeuutPML4mfj/8A//8LWvfe0b3/jGzTff/Na3vvWBBx7gdSIrPPMKAT02EjCDXz457hMAaS05J6wz5j5W03v37tWb9tGmwPykABgGQzvks+QmkmUs4ImHvB3NDewmxI73lN84Tz719RCYV35bzUvuHClILMFsohv56bA4Ag4a5sidUfPy7YOpDAkYkRQyqf19ssHfA49vferpZ1evWHrFJechZUJAZnoRkQ08YPIs/sWqGCyVAagAP/mZ89mXpjZcgO0AgSJaFEuteNasVU0W+tBiJIl7Dx5X0N1UW9A16He1Nzch4G7bbWHPtm+U3ar8jVE2PzTYiQLbKjdGYmx1mMzkGWMhsmQ81p2/2vaeT/x/u7A6E8ZYSGRynKBVHrOsr/zwbmes/eFnNo6lBTOzaASQSRDq/QFmtnn9oeeH7/nlL2Jh71tef0tXV5s3HP71bzYyJVwpJ8pq2T1CZsRlpCX2XaYRtdFpTYFcDOfwYZaniMxvuxleyT8GMA72YWQ9aaVV6/7w9NSbKNTld2aeHvKln/xKgKDj4+PYVTFgpO9q4MNAxtX5nl27doGOZEyH5vzNb37za17zmo997GOgJq9w80//9E+vvvrqK664Aj0zauqPfvSjJML90dFREBedM3BLUsQHXzlROOcmaRKHt7q6uihGY2PjNddcc/JraKdoU+AkUUB5lhGbKojAHbihObLMLp0cHfDJpkcFj9+XP9U20NOVMuUy/LaKYdWZP6CM36GBwsPNTRWO8LQau1Kr6uXs/qKzNWNrRFerp7WhORbkwx/OWnf99Od87Ddee+WVa8yGicY1FMUw+CdgqAWp8TsteA19ZyhmrVwI0gSZ+jWpwWdAXwKK7p4OvIkIgQguNAQIqeCWgLsQweSuOXNaiAat9SuWOXK5R556IeO0do+ki85gd8WPh2Ai6bQ2tTLOGE+nsehKIdCWre/97J59I4mvf+8nW/ZPOQIhdzCEkBsJWP/vGxs90ZZB/GEGGvePpkB+GaBIrk64Kuj7/IHi9+/6ycCBXVdceM4t17RduuFcpgQefOQJHuVkEh/NBCr96g86cQ7zpAqiOWA4wLJmD97A+Tm8vnShhHczfpkcfuWlzixM97C7uKEJyZkfPVYqbodjUqDWIY8Zc44R6A0NDQ1/8id/8uCDD27duhULqW3btj3//PM/+9nPsL3av38/RlUKmc8999wb3/jGtWvXfv7zn6dbA65kyUpf8JtvjHRQVmN1xSsanylhzgFpdM745cBR5bp16wBmovGRqKgN6IL9ZMrr7K9JpnOshv2aTYHTQQHYVu1Xyw8mXEJKWdzZupgNdyIIN2gsTahFOUUnFUtnuERVgSwZmcuZcjRcRd6tDzWObBTCYtBbn2Z9zJnOkYBFrsWnVj6HE61CcmTPrt2f/8JPt+/YdeklF91w5TI0bHihAne9Pl8efZgBBuChig1yokELpNCrT1X9UH1+6F8FYAEkkybFYC8MhAHU7C3RysQyj7CPM0GFyEpzGkjktkiZbOV0yXnr8qnEcy++NFGyRtLOXMnTGguJ2t2Ug9aN4fHS7Ybr7RgXEP23rz6YzeVXr169acvWL93x/UmzBRP4+vQ269FHH42FwyvXrNszknhh+wEqxUpxaSO3aypvDeWsH/zsnm27dl9/1eVvfPWVzM1fdtFKytw3OsaWycY9N3p4Fo1RaEOJImpoupcD3ybstZgpOPKWG9/d2LiZH75WnTQdQrDL45B+afqrVrhGWLlUQJYzJbOc2eFwChzykRwe4UTvgIiwCrxIXnTRRUuXLmXNLrO52FKx3KijowOjZR1Rgs1YPoOgd955J5pquoguKCJ78Bsw5iZzvaA4s8KorxkFIyj/9Kc/JQJ4zJ3vfve7559/Pi/qlDCZAswc6ceUgVyw9rrwwgtPtD72+zYFTi0FQA9h19NfppGlYM+vvemG99z+1nPYaDAvS2vc6IFObUkMHlTgluLUfoflqtxWoewI0eq5tCaieKeq2umKHpbuEW6YrJhedLCE8bwVnnJqPJdOPf3s84uWLn3jrTe3+kVXT1DAQw7VJMiPbCAXPz3R+xxrj7Q0tfuHn9QDsMG5AOwFxtLYEAXYNGV9S/ZCQo6sYI/455LyyIADZBKp84IVMazC+kaGd4/iDtqdLbgagwH2cVQApnbEgV8VHK4DCWvHpMUyEFxo3HjtFeefu+75bXt+eN+WJKK0Zf3oh3eFnPkrLzznhuuvGE0Xn3hpFzfhgVYqKUl5rCdfSjz67KaVq9e+7Q2v7WmygNPFMauro30ynXtum7ikFt0x8RlVOFxItLJ1hNtbdnlKTlfJ7Sm6XcRhtkOkcFTfuLDOiSsP3pJqVI28ReldC6aFzJWMg+wwMwWOr/fPnNbRnqqGmaecIMtyAmQikhJAVgRWZGJEW4yZP/3pTyPUIgozcww2o17+7Gc/++tf/3rjxo2sNXrPe97z1a9+9a/+6q+Qd4HV97///fTLz3zmM7z+uc99jhc/+clPkjgSM0d6IVposgB9GatyTtYsZOKRHWwKzF8KCNzyq7Au832KLAV/X9UbOW9VIydiYYwp0+mZAYbJwmprof68dvN4Tgx/xou1yFWYPh0vi2ZQDXUKuI32+jDicudTpWK2vbPrhhtuWNaFm2qrzLIbdlYw+nmXbOhLdAFdKbjkLdpRfghv/EByPeEoVkgV4h+5PiYFKS/MhD/o2Jj+xbilp6uLR/rTN838VyU1YlICAWBDR3FnxrZIbmtFT3e2VHh2y2T/RKbk8DeFg6Lxo2rloscpWnQ0fGyXxCzv//vqPZFw6IoL1t54Scutr70x2NTx9R//+qn91r1PJ4b79raEHO+8dWlPu9WxdPXWfUNikEaWoQirTEDFBx99eipbvvzKK3tand6S5clnUZVvuPA8djJ+/qWdyNbs18SiYFx5AvZlNjl20S5imM27Arqomo0em0sdRrCSGhGfczLRqlE7j0+HPdyqAoo+NjWyDzNQoEqvGaKc2CNFXNMjRXekWEiS4CLoi4UzAuszzzzDAl/0w+eccw7y8VVXXUU0hFrUy4jOf/zHf4xoiwC9Z88etNNqn0UKiLPYZ339619HiX3HHXd88YtfRKRGEY3ErNmRBTitxWegSnaqlz6xCtlv2xQ4dRSAmxnGXcGJWkYyDcyPz1WMCzkT7ie72ZzqoEwW7BCOWpV4JFMt0JGyl5LVB3lT0UjvViRCZej1EWdz7jHDDkQ0zKEJLOlht95Vq1ZdfMkqSsTohNVHZOh2O4qy0FEgVhHXrIs1y2dNYYyJMrPpaGFlz0DjUZkj2t8ZSyFyraj/OSJFcMIeRIsX9VAWGA2jCZ1+he/UUiEqT+UFaTWiiHQLXl254QL2T3j02Re27Drg9IZaY1FkYoIxrsJO22I5Zb5cvvuhzU9u2mrlUr//zpt4fsX5Le2LVxyYzH/1+/d//2d3Z8YHb73u0pBldccsbyg2NJXfs3dIUiE3tzWatzbv3BuJN61Z0ykFSk2RB1mvXbMyHItt3r67b6oyOIBS/GhfjvQvDLMHk9aeodSeweTe8fJQxhrMWiNFgWQKxp5TyayY2FApaUT+1wfu1v/qH9nnh1FguqMc9ujk3ADzVMmMOKt2WCAramS1juYEqRTTKunKxpYKKRnURMOsYImHDdYg0dexiAaV9UjJEG1JDSH4fe97H8IuQE5S3AfOsYjW/ZF4izskS5q8y7koZ+xgU2D+UgAeaxSVpp9yUf0ri0nBHrieueNmewMEkYAx930ZanP0z8iIeQo41XJp5IpsJCDDDf5QF7k4zpDN5liO7PH6md1FZPzAbbc++dLeN73hkmbZBNkKsloKRDVZCEvJF9kizayHOXJWR6uHqBmOFIBM3H5lZdmSFXAV/cW0r5TrbGzAjAmDaioG6Is7En4Vz1oyYyzYLKnJnDdLuTnnt27tkm/9xNq+c0fGnwy4y2j1SJM0cuUyrIqT1ojXV0hsfuaJJV0dF/WGMe0mVWTW99x2zcjE1P4dW/OTQ5evWXbLDecRGcjrboruGXdv3Lz9gp4WVzZXinif2lzKWI6O5ujSBqvig7uMA2urM2gtb298fvO+/ePWsoi8i1aZcQplpQwTWWtgLPW/P/cF/HOlMhm3x+dAF22Yc9DraQp6gj7/ilXnXHH5pSs6vMC5ToSY1kR1TgIsZ6J+FFb8jtIWpE8JCdyqHeWuBN7jp8FERFE/l35RTeNM+0ttT21Q9CUP8FJzUqSsCaPqyZlHGhPU5KT2lBPFUUVQPRK5Jtoqpmqamj7oywlvAdKarKbAuQ3ASiL7OF8pAI/2mG104GxizSPfp7njdzvgy0AOHqDEI5I3AlMT9neKa0L6lIFj7XdQlrW7nEhRK9GmY8t9Zal6lCecUTUKz1HfMy8f+0BktjLiddIAV6HGdes7P/aWSxe5Zds+fGGQIBIyimgwD+Rwetg3SvxES55q8yWTneYncbUUFMTQXOgsCYAIAKoMdiogIfnxgKuMwzmFG00D6U2efDQzGM6wj28jA3uQBvHRwTbJgim6F5KPd/CqAeOT4T9DEABYXKgUybKjzcJElIXdjol9/uxopKVpykifwUBwki0Wy9Z5XZHm7IFlwbRnqu8dt12vSEXiKyPW+268qNMajBdH3/HmNwD5kAVR4/VXrHGO7H3hhR1s1eyMeCcs69fPbxzN5W697oIgJSthTeXPUHTLWmSVr+htjDY1/+ihhzPasigE8HNqWcjEd/zwnk//7T/tHp7yRNvbF6/zxzuxESuUvWy/lMi79k449iRDn/vhY//6s+coMPsZk2KhhBMU/Jxlx6b6LEfacuCUyzlewADbuuvh53cmxfNn0oweyAL1QTYF4BvYZ0YSt+BFFh+nrOSoxb7OMgtwFoVTLgGfRbS0q2pT4CRQwKCFSacKTsI0OQcDKkHcIUmoRtCrU3WcbS4zxQN7potXdzp9c5ZntXc5EZQ90mtVQgnd6sIhl3VP9FSTVtCtHM0rsikGuFB2OV2IuVwwrud50FVs9JddhRKyKYDKfeadUYBLkVg1K6IvQdYjycJaGQFwdOUS405/iJEBkHzFpRd9/2f3+DxlRzHFXg4kKxAOWrPUTKJOedLDicnBD3/4w0GPhXuPsNsKmT0erlzb7nrTa7L53NKeGHsXSmQUyx2BuM/avX8A5Ms5rCQbQ+3YhSOz9rCviTJlEYZ9OM+iqDGP46pzz/nfdz/z9Avo3C+fwqLAyL6PPbf33oce2bJjdypXeNttty9etnxFj8SnJpQqgcOQhDUwUuibKAx/7+cv7hn8zabydWtlvXcxV/D53el0Mh6J5hIpb6gJ/6Wb+q2//ecvJ9J7VjzScetrbrl4dTu1A2qZqPcF0FTQeBh8hVhcJ+fI0qKCFxqdVeGIHfisooBdWZsCNgVsCtRRABQAEg4OslbW4JxiJLDELHMpl2lpjGOBxBugR1HlXl6sTHUdhCi8TjQvnrmcbjTJoOY5q5Z941vfQZmHR5EIVnW8aLZ6JCa/plhk3eoVzLW+6orlvMh8NXfxwYIQjKb9yksuwKcR0UiHp/mC1R232ptjW/eObdyZxz58687UWN/+Rb09S7vbicA8nDPoZc5CNkByW0uXdHXGGzNu3yM7ChuWuvcOWD/+8c+effZZLFhZCPr+99+6fk2cIQLwQB0QSlE7NHusXNxaEnePWu4Dg+d+5wc//MW91lVrX0tdor6IlSmFiwEr6/L6wxMsUola33zw4e3pyUZf7KXdw//7S3dcfeUVb3vDpUGfuO7CA3je7O3UEELPE6DWlBCfaTj08Ind3FkUzq7ankUNa1fVpoBNgROhAJhAAOIklFjJiAUXnq1BJKAQLXfI42LudvXyJSi9iUtEEQY1sE4W1FLQNpOaPEFENr4m1chO9opY1WOtWNyNl4KVi7tIkx84p9lyvnRR/Hfe8LpYJAqPxlFVyIUQywaMstevq1hGSMYhVRYf0k4cXVrMiIPfvT2dexPlxza+dM6KdQ888lSIvTFWLu8KS9mciNwOMeXL5YoZrLh9nisuOPc3z2594vmtP/35noH+A4N9fUG/+2233/66m85jeoMRhgwyqFex7C5lxc+Xg8lpT1J2d7Bec9XSB+5x7N699dFNY9eujSNwM3GIZ0upJK45ou7/9Z37f/zAb845f8PH3n3T3T946Oknn/rVvQ89/8KWSzZcEAn4n3v6KauAI4/c1VdcduN1ayFFJsvkgoXVupCxSsWz4a8NwGdDK9t1tClgU+B4KFADgdoJGIxTZodLUNUgLpLlNZdffMG557FtAibZrBxyGps4Y14KeIFdvIwAKfgHMHEUdMEoNJ/yBXxIrtxct2LRzpee9zk7FYCJzAn3eYoAet6qLt6fmEw3hANwavEBaZXcqMQLeTceSco44xSzauL7nWWKd86aFfe91P/kll23Wus2vrQnVCpde+4qilIEnz1uK58E4XzektPDAirr8vXn/PCuu7+9fXtTS3PA677mla98x1sua0TMZatmNN7potdT8rnwOYg6nbGHMSAvZILeONkxD/2ay9b98qHHH3rkkfWrXiN2Z3nZ3pgFTJNF67n9uV889Dh2Oa+47JJuv/Xht1/12Pr1OBjuH+j/wY/uKuayDbGIo8gAojwyNZkmeSbvuRCSnXXBBuCzrsntCtsUsCkwKwoI+gI3BKycy9hPwS5BxAwzqQ5rRXdT2+tvCQUEfdR1o4CtiJg5n66LFdmXIHPIoAtHYjLdyoJjTkgKz8ob1q7Ys33ZeWuXcykols54/CyhkrVWxOfIzaDHhWScmJqKhANqwoSJqTxn4wQv2lzmmN0htyOTL527fk3i278cGhm798lCKu+Ie93nLxJBVg3XssWcz0oHPWZ/pKJ14arQ1eetHU5nl6xY+apXXrS8VfLih6qZY1jqRamxlsobT9GUnO0LnVYhHXEzenC++brLH3vsqRdeePHJTddctT6Y91hRj1XyWQeGrb/+p89hjP57b37LDec1MoygrNeui61b/K4HH342kUq2t7axfzSSfdDrXdLdgDqd7HwcyxY7V3kQ28+mYAPw2dTadl1tCtgUmJECIt0eURbDmLrA9rsAMLZYqGXxj+xoDBr7KrS1bFFgsFkgU2G3ir71iSkGO2SHKDDYGfNYl61vX977wdaYmF9zlw2f0RNzQRkwmMbDlMvjYlMp4nvFZwnjgLwx72KpD9PFeMRgRld04g4HGx87mjxWS0fn9rHiz+79LVrhK9evjeHX2cxM42BSZoCtdLGcyzoCeBIJW9Yf3f7mpKPU0yEGYxhYNYUrJlfI2ZlUwoPVGU4JMbl3krdTXJvI9o6JQMjpKpeWxsNXb7joO796/Je/eWLF+qubvNa+JB45rK/85OcD4xPrlq+6/ZJ2zNRlEww2hCha/qD1u686jxn0qZwVMkuqyFSHHfj+MCSxIlyTi4x7zpZgA/DZ0tJ2PW0K2BSYDQVUwyxIIEHAUs8K+ZwTBbDX43O7CuzGZp7yx+txllhbw7Z+xTwec2uLHnlXMEtfrh0xkBbZssTmzuiZSb0zhtdl4FaW6Hr8XuZ0DdAWXW5nkC2gSaRUEKlalnGW85mMB5dZsvYIzHeK8ZUBfLwosESZaec1a1ftfWrH5he3rYyHr7vsYtF0M0MMwiFXS27sj5xyhpr9jCRy1op2K+9EvyxAGDXoK644ZC2zQxxKCxCigBbfWPyYQGZXxUAkamXYzhmdtu8tN19//3N7nntxyyObrmgMpu/75Y937d6XmMwuWbzoox+4rYl3UmblXJnRg0xusyut1+Hwe2XJFjlSGvZR8uAvibqwHw+eMMnULNaqUWvBn9gAvOCb2K6gTQGbAsdPAYODAkKCxPLHrZsbykUJ6VBTZHESJwAlMVziZgoPymYCWPDlSIGnxj6Lxwik4CMBx1kIseK0S2RRyU+2VBLslkQcTjeLfBTKPaGoRGBCVwOrd4yojQTMDf5fdP767979cEtza8BVWN7pK0yWPTEHuyeXRcbkV46EwlOmPjKrjBRtXI1WVlfpRK/E0sRATZW7pSj8DFQ7XS5M0Eq47MIrw403vOqzX/v2t+64o5DcU8yM+73e88859zXXvnZ5h1n2W87Kaw62O5Stl30mNXY0pG4UBvef7NosWcsogeEB88A6JpHHZ0moNuRZUl27mjYFbArYFDg+CoBIAiPGqhmcNfAldwgGP6axVm+aJ9WDvixRzU9kPwBJ0hAttOI7R56apKuvVf8KFJpg5Fci1dLT21xK0Gik0N1itUW842PD5111UUPYcrElMSZafhYWi/W0asorSVSTFqlXzjHPlqRMYrWyiNk3F6oh54Qo6VwxwOrjoJexx/nnd6+8v+PA4L5zl/Vccv6rLrloQ1uDTGAXWJwErvqRyas0kWGHlLauAuZUTNWwJDPnHLk6m4INwGdTa9t1tSlgU2D2FFAwkKMgh0CUgRBzXn+oYkzlnkaejmCwpXopojJcl6MsCgLYZGMNg8cmRn1cwI6UxYaLUJdo3al5xEHjkBpmzBetWLR9+4GLzlkm8Yqor50e/HdIFoiYwL/MW4sMyh9yZ2ExS5oF3jkyMsDI2yQmR4FkzL6RzsmfcnILEdUREs9aI4mUJxzsbrQ+9cdvl02WGq0QGZiiyfjCR8yCkyntimNNJGCpKa/zq1RMMnDKoMBQQe6aIkmxzppgA/BZ09R2RW0K2BQ4bgoo2gFFRjiTo55Xj7NLUFMxcWG5CpcG4QR0OKk8r4IxEbk5DUc81ndmyI04JN3qtN5183U79w1fuj5i1OJFr18mcU1aaL+ZZGVJs4CdBBZFVQHewJ8ovk0Fdahh8kQvbQIpYG6dx40mk8roz8PB8Uwx5netahLTLePt0iipi5bHKxGSTOo6ilHWMVXnwslC0VcT1BqSuhQG/YKR8afrrJEW+tEG4IXewnb9bArYFJg1BRQAKvg0/ZYTeAAdBdIAy/rjdJwjnB0CJ4pApABYmkdgkGbFSUXE1KccDTBLNI3MceZATLh5BJ+UrdbK1maBUnG3nPV4fWzNEGQlExOxZQyTxZEpQGiQV/I1ESujAAPVsuGEMQXnqUQ0G0kZydjBymDPeK7g9gpUN/tdTGM7c+UiWumyTy64C5SWSi4nvqVxEOLHvKxGTJKrEUSLp1mbPOSUmyYBvX1WHG0APiua2a6kTQGbAidCAUWRGpbUgGQWaYI7lYCMRwp6XY9A+pj7mj6AZHBXMVg01QQjH2pEiXZYASRVFwI6Gypm8p6gR1Jz5jOlHABbYMUxK4QIJXGbyYpb875EwRJKtMOmVJo7RymA5EcBwGyz24PIwRKNYYG7VM5my0GfA2tsB2ZWrrIHN12mQKmJrAv3HWFZMI3LZ62IeYuDgKskodnIDdHC65WU/qwMNgCflc1uV9qmgE2BI1NAsEDRbgZsECAxQYGwDlMOul9BW3lsIEakWsFg8wPeQCjuKyrJsYZDtfQrM7I8q4NcOVVlOCcadOIWl5WsD2JnoXIQuGN/o2BIdjkMgJZkSeqaNyuHBfmY/8X8KiePHEi0gKibyPy4oYGMUDuzZ4LZMEreL2bSEW8w4HSI00i2VkgMkYLlDkz6m4B9byMeNQhpK5lzWV5fAOts3jKLlcmNMksBqpVziEMSEcRN4Uym0KQC01qABX+0AXjBN7FdQZsCNgXmTgHgooaLmkoVQGadJjhWxTRNrQqm/AWTUNpWktKnHKs36rOozkPrvZp1MaWrQRqPCjnZsYGbxfx4MhmJNoKTxn21SZQnmjTlMfp0KZmUrTLpi9TLlSK15sOliUuhpHQulkyBqsjZ+MUmqVBE/E+6vV6Hl3nfjBFzJaYvwCZPklAlR854HXDnukpOSbeWe0XQrySgeZ8FRxuAz4JGtqtoU8CmwGwpAE5UgkIVF9O3zJPa/WrEI+KlPqy+WnlH9ng2MqK+Yp6aRxw0qj7lZXP7kGU51dRqGVdOqtF47g+IFRWJOVzxaBCMMztAmOR4WktAQJ8LzKc8lZti4I0AKsXjpyCp0U1JqtXBAxiT4cSQiWyz87JZX0QqURPFlN94sxR85ZbmYs655OYhQeLUZ6h5HhJpwV7aALxgm9aumE0BmwInhQJ1CDTn9Cq4cnBSB4HNwY9qGdXFOXKMqlEYbxBB4ugrcjzojYMuNFoVubmqBo1lcLR666C/1VckHvCBQCyPiV/3iokznd0xq0ACdXEkvbMlnKXVPlua166nTQGbAjYFbArMVwrYADxfW8Yul00BmwI2BWwKLGgK2AC8oJvXrpxNAZsCNgVsCsxXCtgAPF9bxi6XTQGbAjYFbAosaArYALygm9eunE0BmwI2BWwKzFcK2AA8X1vGLpdNAZsCNgVsCixoCixYAC4Wi+z0TNtx4pQlaxIcZu15JBJhg+sSfshZQ85G0CxOYz/to4dEIqEPNVomw3Lz6Vc0nfoIR09p3j2pVa3ALtwLImhzpFIpbaZsVvZFO4Nqp0XVYlMFKkL56/vYGd1KtYaodbwzpTr1LKLWHEdkCGdW1fL5vPJAGiKdTnNMJpPzs1Fg4zBzaK78nL7kdrODk6x20hahLrWmmZ9VOLxUCxaAa6BLgxGoOY1H4D6XgUBAI7CnNI8mJycPJ43e4ZVwOMz56Oiotq6+Ut/Smj5xaidHS23+3Ie58+Fp1TjXkitozZ9CzqEkNCugFQwGtZl8Ph+Ny4c6h6RellcoKgWm2OROFagI1al15pelSCclU+1adDM9oePR/c6g/lb7tGsnkOUQhgCL4CZVg2mcFKKdhkQ8Ho/X6wV6GfPBFScmJkKhUD1zOw1lmGUWUF5DfXzu6NcB+lIXPh/6FaE+znw+P2MY0/ESUUdGvMUJ/QlpmK9CW+uxxx5buXIl3I3eRueD2XF+NAxmPNjY2KjIzXF4eJjWbWhooL11/KWdtSZt1/I93gKf5vjUempqCubO9waXp/p8gS0tLUejw2ku3pyzg5XEYjFYIQ1BXeLxeDQaHRwcVDyec7Kn7UUwqbW1lZKPjY1RcnosrTMwMABzPG1lOBUZUf4DBw7Q6+hvtA6fFfUCq86U/kZD1L5x6APT5w5CGEx/fHwcNkJP08Ef0agg1TwVZDzpaVJ4mgaGRovwmXR3dyPBw+Lm25hV2Tg05wQKK/GhP5d82txR0IVFw8fQcZ50Qp2qBKnJwg40CYE60kL6tdPJatTky+F8BglDUVbj6xfY3Nxce53m13NOaue1p/P5RD8wrQtMZD4X9bjKpqNghVuQ+LjenVeRtfBUhJZaYA0EnbXjzTcuP3MHqP/G6z92rYsyB1Lg0RlULx0oUOYaD1SWODMpXt6n9cTn03jiiScQJGoohlxRO5//Jw6K+PJS87TlTk0ZMe3atYs+x4hPlXvIrwz9VBN7xJIgkdDGvAJ+8zqf2ebNmy+55BKEEnoqXxopkDLdl27BeX3nOGKC8+Qm9Wpqatq6dStHQl9fHzShIirQz5NCzqEYjOKXLl1KKzPM4hy5hCZDXXGm9HP6D/2NISMSMKLwvn37Fi9evGPHDs7nQI3584p+IMhbHR0dIyasWLGCv2eKZkK/cf1AaCOqAzeAJ7S1taFRW7NmDU0GN4AnwE/ob2dKvZBJGOqp/pbvZcmSJXv27Onq6tL54PnTfygJxCdAfIKec6T8KlAh+NIEcPV5VeZjFmbBArA21RHrTzvR4fh++Iq0wWi8o437aGz6IqozEuQVTniXt+CPKp2QheKudosj5jgPb6rymYJRO4YXKM0AYG6e6apOeCLtQkWAMRVEmDVARqH55mErHF4kepEWmEf0N+1sjJaO1j8PT2F+3qGb8dUAwEzoAFHazTg5gzhmje/DN7R1aBdGeNrZ4CH0N+JwQu3OoP5GXVA7I4RQcr4dJuZ07mN+dqRDSsUHwifPx64sGvoTgRY5U/rVgp0D5gMg6KdS32aMmOheqi/i+6H9aDyCtlx9TD0nEU40Hd5CvwErUTGx9goneq7HwxOZh3dU9QRD5NtjCEzJ+fDOaJ2tElnZB18gjcWQgvYFfWmvw3vCPGwUikRRKbB2S5qGwSLdlUqd6RIwDJFa0M3oYwi+sHtaRzvh/GyII5aq/gPXc9qLsTiIWz/vCNOoj3nEpObJTeWHqgLcv38/yjC+HbrcmVJ+Bqa1sSkDCD5zhkFnCvrSBxasFbT2b4VPjnwnek7fop0AUSIw3OP+zF8CUoiO1umXxOQz085KapogN+vPZ05t/jyl4lQNJghDRBChCgsAfSEvbQpPQS6hatSRoRUwdqagL+WnqBSYYlN4qkBFqA6Vmj89Z84loYPRzehsnFA1ut8xv74553WKXqz/0mvnMARFX2URsAuqdooKcNKThR+SpvJDbDAZG7W3tzPsO+kZncQEa5QnTZ0Z5ATi85kAxohVJzGvU53UggVgGBlBx3Eca+cQFGWFYqqec+Tp0QgNK0QcITCqArlVafapT32KT46hFl2BF+mvZAHfPFoiL+N9OqjmXmMK+rFRZYpN+emyn/zkJw/55Ga+fBmrc3jW6CS0jvrh0V5/93d/R/nVFoP4tQHy4e/Owzv0MRg6yKRdi0r91//6X+mxWlT6IZXlERWcz+ilhafMlFNbp9br/vN//s86nqCB6IRaC+IQtAURKOdhu1A8SqWchE8Jds/nT0X+5m/+htahLjytCV6qYJtXtTjki66VjR7FOQ3BCR3vz//8z4mpQePU8AzWV3vr5T2hFQhaBr5uGoLzGvFrJy9vIWeZ+4KdA55l/Y8ZDVjlcwKw6ZT6XfH50V8ZKqIq1H7A5wff52uEy9Sg/Zgpn54IMI4aAvGN0VkpMzyObkpFDtEBch9uQkWoIMUjAq/Mtxop3SgYWAXZFZxgExSbm9zBPg6FrTYNzacai9ND7ZOSC62A4KtJCS6Z5dpw/HrOQm8kAs1U40QnJesTTwTopcA0Ct8CLUI56XLaOtgZUC8KrJeal6pAD8kXXn9Izzwkwum/ZKBAfzvkW6CydLna99Xf34/4SNlqvOL0l3PmHCkwQb/uWjmHhoaQfbnkEcdaj4LFIWbUajdzyvbTuVHABuBj003Zeo0pYCtYm5ADeuEUdGji8IkqGBw7xdMbA4ZI4eF9FJJvrP6L4hJuCFshDtBV+/aoS+0rJQ6sB2Z6eks929woKlWg8ASKCpdnHktfpr6ccP+IXH62GZz2eEr8Wn8jf52eZzxEoF0YCOpYsL6ZTnsxj5ohpSLQZw4HURqIR3QzagFC0zQqvvBNMbxgqAR0Eeq76FGzeZkeMDwClmgIvgitJp8P9WKox30KxXLnzs7Ol6l0M2UrrVIqKcEPiccHQuH18wd3mSCAIehoAxan9eIV5YSHvGtfnggFbACeFfX43ui49D/YisqO8Ef6Kz2V9+mssAwezUMAhjVQPApfY9nCxQsFQIvvCg5I+WvfGDGJT0z9SonGCQxR350VpU5XpJqYqOBEtjWUpcAwGq0IJZ+f7TIznWgdehS1oFIwepV9aw2hbUrbcUdbcObUTv9TSsjXAeWhP18NR+pC72LGkUseaQfjI+KSKWFl/Srl1xr09Bd7hhy1VFovolFsKK/6FcZ8VIE6Kh+oxZkhtdP/qPZFaNa1QnJS4w/Qn4aoSflUGYZG96OavKUzI7Vx+emvwoLM8ahznwuytnOoFF8ab8FKOPLJ0QvpgjA+BEp6J/yR+9pBuaQrzyGLU/oKXxQFgztQSD42Cs8dGDqrQfRb4jOrjXApP5H1PhVXLkl8kPiUFnIOiauSFraiViSkUGMcNAotxSOaifucIGzNIYuX5RXKTIvA9WgFWo1morHAYwrDI06oDufAMxHmIfrCxKE2fYZC6gCOdqG0tWGEsnuglw6JiMwQlnrpV6YEpzdqffVynhxVmlfiK/pSML3JkZbiUr8y6s6HNk+KXSsGpdJCUjbKr58GT2kOehGPaDW+dxqL7qffC6MK/fBpPp3HqW+mWsr2yYlQwAbgY1BPMZVvjAkeoiJ4MWVCN2WxJkftoHBJuiwgTU89RnIvx2PKxpfDJ8RHCONDxUQpWIDEV8cln5kWCjlYPzPlMjXc4kSR+OUo+1HzVDSi/FQKfq2gS9PwgmIzHJ/m0GaipY6a0Dx7QKNoi2jVqBEn1IU6KoyBu1RZa63jwnlVA4YLlBMYRqvMV4N0SBNQQr4UyswJNznnSGejW/KU3sUCGLqfCvpUkPrOq0pRGApJH6N1ADD96mkaaqRjVlpHxX0KT+QavM2fWtTGBHzOIK5+0SrjUmb9TJR90f34XhRraRSqQO20RozO50+NFkZJbAA+RjvCKeiLfGaqbf7c5z5344030lNf9apX3X333UAvvZMk6LLafY+R3MvxmM9Gh7T33nvve97zHsoP43jkkUdqZeEm1USU5D7h9ttvhyHyluKWSvm1yPPkBK7xjW9843Wvex18nFa4+eabv/a1r2FLglxFi7z73e8W/15NTR/5yEdeeumleVLm2RRDMfXjH/849fr0pz9NjajpTTfdpGxRGwhDYqoJSs3DgZHiEFCKncS2bdv+7M/+DOdKnF9++eVPPfUUwz6IAIBRwbVr1/LJfOADH9i0aRPevvjEdORHhNrJbCh2euKsWrWKJoDgNIea+P2f//N/wCf9mnDGhOAOPLOgAArMw3ah50CoGgxz/vTTT//rv/4rlqR86Xzyjz/+OCUn8IharF69mlc++MEPgs1wBkVibb7TQ/CzJBcbgI/R0Az9GDDCKfiovvOd77DE5UMf+tCTTz553XXXveENb2CYDx+k19Kz6a/1/fsY6Z6ux3w5sAyFUrj2+eef/4d/+IdUB1iltCqOUEHucwf7EUyIGWSoLzr96mo63tNV5FnlA7Xh8v/lv/wXRhXbt2+nOQDdZ599Fm7yt3/7tz/72c+++93v/upXv8LX5rve9a5ZpTg/IjHaoxa//OUvzz33XMRHoIjGYvx02223AVQ0GZWlBYFnzuH486PU06WA/iot0XmuueYavhra4je/+c1f/MVfLFq0iFEs6AvfB73+8R//8bnnnqMRL774YiRmRkv6IvVStJhOdB6c/eIXv0D7hYtTBN8f/ehHfFMXXXQRoEtR3/e+9wFmPNq5c+f/+B//g69Jxcd5UOrpIihJoS2BuzAuavGZz3wGKQLo5fN55StfiW6M5uMmQ9vPfvazzz//PN0PzkDFabiaEDKdqH124hTQJrGPM1AA9sdT+uL1118PN9eYiFm9vb3/63/9L9Q43IFXcuTD06fz80iZtWB0GwYTOm7gU3zHO97xe7/3e1oRQBoWSTTqy5E4Wv35ViOYNUXSGsEaOF+2bNnnP/95HXD88Ic/5A7ne/fuhVc+/PDD8638SmRKVSOvdh6GQYiMjCquvPLKT3ziE1pNxEe4vFaBFtGmoXbzrVK18tCLKDw8vdadtKWoLOXHWTfoS2Ru8uEg5f/bv/0bl1RWm7KWzrw6YS5Ay/PhD38Y8V0/H0YPrJ2tb835WYVa4bUh+NiRHL73ve9RIxqF8lMRNC5UCm0EGMyQiEeIv4zR77rrLuLPc+Y2r7rK7AtjS8DHHsPojBSi4T333IOYRV/Ud1AM0oMZwtN9GSHSd+eh6omi0huUWTC85ZLvDaEWJ/Kc8Aj2h27tC1/4Als0ondCpuRLIz5DZlgJ4hfVp4LHJtPpjQEIkaGKgMgcd9xxB6IhHJ+tUbhJRXiKmEjrUEGUn6e3dMfOTUtOLSg8DQF/5BzmiH6FQd5VV12F0oKbjB5Ii7ZAmkdGBLr+03/6TzBHrR0NdOycTm8MLRtcGyGeVmAKgJ62fv16BnwUhL4ET2dviRtuuIHa0SFpI9QtDz74IE+prH5BtU/s9Jb9GLnRl4hB2b7//e8za6NFXb58OVIvoHXeeef9wR/8Ad/LPFRLUGwtPB0GmvOBU3iGO/QopFsahTLTcC+++CIfEaI8EzqqM6Nz0oi0DhxDXzkGjezHx0kBG4CPQTA6K5yRI0yQTsleLmqlST+Gs/OIfsmRp3Rioh0judP+WMsG0KrtFfkDrqpt5ibF5g57uSAyoolCefutb30L2QtI4D5Vg0vqCcd5FSgb5dm9ezdVwOoKAZF5AXQSqAFpGmbstC2YHoa/c3NeFZ7CwP4IjGzoNvQrEAvejT6QeVM0zNQO2VFHdbQgnB3h/te//jUN9PWvf/39738/MEDTzENer4yeej3zzDOALt8IlfqjP/ojRq5onpHaaTKqz32qzAksft26ddznnErRmpwzIuRyXgUaS8vzwAMPYI9JdSgqd66++mrw+Kc//Smt9oMf/ABgJqZ+NfOq/PpF08coFd2GZgJZmRegtHwp9C723fr3f/93qsYnw5S2VgHcpXXUigIGMg/727wi8hwKI1zMDjNQAAbBUz42lVReeOEF4IoxIx0UkwS6r4qJxIFp1r7SGRI8zY8QqvjYKDD5KuLC1qkONzmBuSN2/If/8B84AZhf//rXv/rVr2aEwfQPKinegkvyiHdVFDvNhZ8hOwbsVAFOzUaK4Cu8njk5ZklpBQJMXKsMjAFv87BdlJdpwYArysw8IrIv/B27GCpOyeljDCY4f+1rX4uwAqNEzII/comiQqX8GUj0sjyC8vQfqkPu6FSwmeDkwgsvxOjvvvvuY7JDK0vbcR/9M12RL4ibdFTFXc5flpLPnCntBf35Fr785S8jIGKHpRX5/d//fRUrmbPv6em55ZZbaBoUFTOndvqfaovQ3/iQ9VtmohdrPkZCPKIvvelNb8L4gI8FqGYkxH1ahI8IEVn7KrqK01/sBZ+jLQEfu4lVH6s9GNDiBe6ATPRj+KMOKuE7fIfzcIQIp6O0lJlvSdkc5eSSYnMfSOZIoC58ezzihKoBaRqfmHyQ+sVyPn8C9YJNgFWwe7ZnRg14xRVXIB2iDGRgpI1C7eAaaNV0x9D5U/haSbRTQX8oD+JyvOyyy7RfMdTDwoBqInmAvgw4GALCDRkhwUbRFpIImoxaUvPkhOagJDQB2gjsd+g8gBYdDy30z3/+cz4QGohLJC0qy2AC7QVWCDrm4EVucgdSzJPq1BcD+tMoCLsf/ehHua8SMEcdJGGcRdNwn+2B5+GATwUJepEWm55Di+jUOzoJtbfiY6EhaDICFQGAOdKUOtSjmnqHm3Y4WRSwAfjYlIQpEAlYYuIK00G4g/ZFuu/v/M7v8IjvTfkOHP/YyZ32GHx7yhFgf8Aq4EQ5ET6oBXd4qhyEbxIMZqqbO3B8qqm4y00Vgk97wY+RoQ7JaRcd91AphEg0ZryG2pNawGvgmPB6UO0YaZ32x/AyGkUpDMqCxMz7Yu3y6KOPYjOsGoh3vvOd999/P21BFYhDiwBOWA7TIgwpgO15aKAO70ZwZ5yKxSJSL0WljtQOA3v4OEIkYuKGDRvuvPNO+iExmRKmjm9961vpmVRQm3IeClv6afP5Q3Ns4rikqFSHkTfjCboPIuNDDz1El2NWWGtx2vvUTBkqE9MvnXhQmLleVA7QH8eZoCz6cxYdYQC4YsUK1DBwAxoRvTTfFANc6kjr0EYz5WE/mwMF6El2mIECMMra0y9+8YtwQNabMinC9AnURlKED/IRahy6aS3yPDnhw6MkHPUTYnj+29/+lk8L6QpwQohE/qAu8I49e/YwCcT6Zua5GSkDurwIiwQn9Hye1EiLAaPHzhZOwfgdw6s//dM/hdcDTjzFohsxC7bOpCnzBQySag00f6oASVWlSZFomloJa10IuGI5JpdUEEUuhjBYdDOwgGki6/MWDHH+VOfwkjAlzwfCpDVMnEVHCLsciUZXxGyYxsKkkXnit7zlLUAX3YxHepyf9aKxkHGZAmBlc62y3NHlOlTkJz/5CZ1tnjcN3QlkVZ7GOI8BHwNxLEmRhik89SICk9nYzTFfQASmDPiUYHc8AqRrFbdPThYFZF7QDjNTgP4KymocGArCByNB+uuWLVsAJ73P9zlzIi/XUy2hIijsG3EEkRHOqJoowAmsBXT55BBTGP8iiygH1KNWXFH85arC0fJ9+9vfDnegLixAesUrXsF6U9iHBhaKAFQEKshI/2gpvLz3aRTIqxSmJAyGtDwqVDHRCCuE8tgMX3vttYz8EF8QH//+7/8eNym0GpHrR4cvb11quWvBqAu1A2IZRtCv4O///M//XIvDyV/91V9RI74jBGUaCCLQcNwHHjjOw/7GdwQm8dWgnKCEOn6iaUBcxhaoWxDrmQ+m+vOWFTB/QckJ1IUGAnexT+TzQWT/4z/+Y2Y6GNRqBAYZjNEJ2GMy7NOm0e6qEezjyaLAfPQdQZ+wg00BmwI2BWwK2BRY2BSw54AXdvvatbMpYFPApoBNgXlKARuA52nD2MWyKWBTwKaATYGFTQEbgBd2+9q1sylgU8CmgE2BeUoBG4DnacPYxbIpYFPApoBNgYVNARuAF3b72rWzKWBTwKaATYF5SgEbgOdpw9jFsilgU8CmgE2BhU0BG4AXdvvatbMpYFPApoBNgXlKARuA52nD2MVa2BRg50T2wNA64uKAE3w4cMS3BkeW+esjjvhP4IiXKI54R6ndx5dC7bz2Fi/ixULv428B70V6jgcJXDXV4hNH/f3qHX2dcxwTcuRFdYXBea0k+JfQR7U7XNrBpoBNgROhgA3AJ0I9+12bAnOkANs+soEEHqBw4/W5z30O9NV9F9TdLnCrOArK4jEKV0Tqv0x9+eIqi1zx88WRmECpvoUbKYAWH8vsGYzPLDwS4/GRyJoau0URX7EWIOcRl/gf5cjrmia++HU0QCIkBfbrsICUcWgF9JJmDeB50Q42BWwKnAgF3Cfysv2uTQGbAnOjAPvZqSiJZAmqEUiHfReQPsFCvAByCewpygJ+XIJ8nHATqAYm8SqKI1/8IPIImAQyicwJga1BcOjNfQBbMZtzjQnWgvp6TkbsWcQjEJpXSJ9ESBZ4JgsKBvbztJYL5xSPBBGstYTcsYNNAZsCc6aALQHPmXT2izYF5k4B/G9/6EMfevOb34yvXTxXg5dInGAnSIzvezwMc3nBBRewQQ37U5ENyMdGT//xP/7HT3/606AmfrDBbxws474b1GS/mk996lOIywDnww8//Jd/+ZePP/44KXD5pS99CQQFd9lHTyEfb8bs60VGgCgbLoG7gC6I/v73v/+2227DDzDb2eIfmL0uQGLEa9AaGR2Rmmicv+1tb7PRd+4Nb79pU6COAjYA1xHDPrUpcLooAOCBqQAbCMqOOmz6hlgJZLLRwk033QQ8s0sVs7bc/5u/+RskVPCSCOwZBwRy/x//8R8RhRFGUWWzNzCbwP/whz9k0ySKz84Hf/AHf8CRTQxJDYjlFaReVWKzLeC73/1uNiBir0C2wAKM2a8CdTTgShbsEghms63Ff/tv/40N27/61a/yiDhMV7N9FvsysccRqmxSO110svOxKbCQKWCroBdy69p1m7cUAFPb29tRGiOVAsbIl8iaoB1b9bEPEpsFqTQM0L7hDW/gEmxmxyrduKYmgCIQU0E0yWzqx848wDOX7BClEYjP5LEaT5FRX18febFDERI2uw7rnrvIyux/BSqzKSzvIkl//OMfRx0Nyt5xxx2A9Hvf+16AHAH6jW98I2WgVEjGDB3mLWHtgtkUOIMoYAPwGdRYdlEXDgWYXkW3DOKiAQYjEUCRUFH5YhWF1AvUAc9AMtu/o1gmAKtIwOzuB7hyCXwisH73u9/Fkmvjxo0gItZVJAKBOEcyRpAF40mTxImPuMyLRGDLZPTbvE40UmBvebB506ZNlAd4Jg4v6iP2qmObW0oIKq9atQox+tZbb2XbR1TQIPTCaQm7JjYFXj4K2Crol4/2ds5nMQUAVzWtQkgFZRGCCeAi4ibiLCLp/fffj7TKHrQqnqqFFIIyMiiQyVtPPPEE2uNXv/rVyKlohtFUA7fcJ5AU0YBhzkFQ4is2cxM8Zs6YCEp7dNHAM4BKNO4g6YLKBLLjDifcBJJB4m9/+9vYiCGRX3PNNeiiz+Kms6tuU+CkUcCWgE8aKe2EbArMngKgL2Iu8QE5JE6QElwEHS+88EKUydhkEfQ+OIpsSmQkYHBUQZFHmFkhFjP7C6yCkRhzIfJqsmAnAjHn4ChZEJlAIkTDkuuLX/wik7jE4fiLX/yCE/LinAiaODptIJkikSCvs9s8T5mWJvz1X/81MP/CCy8sWrRo9pW1Y9oUsClwRArYEvARyWLftClwaimAthlkJQ9MjrFbBuGwpQLqWMKLCvqjH/0o07ToqH/0ox/9yZ/8CfBJTOIDq/oWUMpCpl27dt111128+M1vfvPLX/4yki7RkK1RXyNJIxbrMl+FVV4Ej1EgMzeMMnnz5s2IzswBc45ZNU9JE8kYGEYOpjxAPlpoLjHv+sIXvvDss89ijcUJcjDxTy117NRtCpwdFLAB+OxoZ7uW84wCzLmqWvgf/uEfECjRQmO3DNqtWbPm+9//PnZSiJvgH0+7u7vBRcAYqRQdNedDQ0PUhiVMYDO2Ubfffvu//Mu/YLcMPHOfOCixr7/+eh4xZ/w//+f/5BWdUQbgkV+/973vkd3atWtvueWWiy+++J/+6Z94Cx9YyN9ANc43iAYe89a+ffuAYfAYmL/yyisvu+wy3sUijKLyih1sCtgUOEEKOPjmTzAJ+3WbAjYFjpcCiJggnE7HAni8rjZTfI+qmuYOgAr4Yb2MiphLhGbAVTPSz5aYCMcq4OpTjqSDjMt9AL4WAYEYDNZ30S2jnQZuNV9VOPOIO6rBpmykSVLYZHGuMre+q0XSFPSOfbQpYFNgzhSwAXjOpLNftClwohQA8xSGOQGMSQ7YA/yARu4TAGBuqsWWTgATkwjgInO3tex1GphHiqlqsaVgqa8TGWMr8BjZF4togJzUiEBM7nDkEvQFthVxFdQBWu6Qph5JilfAY+Cf+LXc7RObAjYF5kYB+yuaG93st2wKnAQKgG3gGTIr6AvykSLQCFiCfNwhYJDFTU44AopAsoIlcQBdcBF/HTxi+pYjKahkzJEUAG/wkvuKo8wQg7U84qjwSWqcE4E0QV9OyILyKPpyyU0uiUw5yY5AmqC4jb4Qxw42BU6cArYEfOI0tFOwKXDcFADzkGvVbEpfBt4UR9X3MtgJzgG9yLVgHliIFTQ6YSKrtrk+S1KricsqB3OHd0lEZWi9rInInHCHR2CtqqBV8K2lqcit7yIxM/1MGXhKLgwXuKzFtE9sCtgUmDMFbAl4zqSzX7QpMHcKgI6gL0AIpJEKIAf6Antc4vICkAP8iMMlcVQ8BfZUwEX85T6Are9yQkziI+mSFNIqr6iQyk3OuUkEta7SFHgFayxAFywnWS55i2gamTi8SEDC5ibQS1ABnUviKzxzbgebAjYFToQCtgR8ItSz37UpYFPApoBNAZsCc6SALQHPkXD2azYFbArYFLApYFPgRChgA/CJUM9+16aATQGbAjYFbArMkQI2AM+RcPZrNgVsCtgUsClgU+BEKGAD8IlQz37XpoBNAZsCNgVsCsyRAjYAz5Fw9ms2BWwK2BSwKWBT4EQoYAPwiVDPftemgE0BmwI2BWwKzJECNgDPkXD2azYFbArYFLApYFPgRCjw/wOUNXiQQgVTHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b962f156-8080-4536-952f-63603d15e2f7",
   "metadata": {},
   "source": [
    "## 4. Present Quantum Results\n",
    "\n",
    "We hereby present the optimization results. Since this is a quantum solution with probablistic results, there is a defined probability to each results (presented by an histogram), where the solution is chosen to be the most probable one. Below, we translate the solution in terms of qubits, to location in space of the amino acids, thus creating a sketch of the protein folding for the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60c1710b-fc69-4866-aaa4-dddc8827e899",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:13:57.469089Z",
     "iopub.status.busy": "2024-05-07T15:13:57.468528Z",
     "iopub.status.idle": "2024-05-07T15:13:57.587436Z",
     "shell.execute_reply": "2024-05-07T15:13:57.586764Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.039</td>\n",
       "      <td>-2600.0</td>\n",
       "      <td>[1, 1, 1, 0, 0, 1, 1]</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.024</td>\n",
       "      <td>-2600.0</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1]</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.008</td>\n",
       "      <td>-900.0</td>\n",
       "      <td>[0, 1, 1, 0, 0, 1, 1]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.021</td>\n",
       "      <td>-900.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 1]</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.011</td>\n",
       "      <td>-900.0</td>\n",
       "      <td>[1, 0, 1, 0, 0, 1, 1]</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    probability    cost               solution  count\n",
       "0         0.039 -2600.0  [1, 1, 1, 0, 0, 1, 1]     39\n",
       "4         0.024 -2600.0  [0, 0, 1, 0, 0, 1, 1]     24\n",
       "52        0.008  -900.0  [0, 1, 1, 0, 0, 1, 1]      8\n",
       "6         0.021  -900.0  [0, 0, 0, 0, 0, 1, 1]     21\n",
       "24        0.011  -900.0  [1, 0, 1, 0, 0, 1, 1]     11"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    protein_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b50045b-3a84-4f70-a417-a321ca938869",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:13:57.589883Z",
     "iopub.status.busy": "2024-05-07T15:13:57.589470Z",
     "iopub.status.idle": "2024-05-07T15:13:57.788101Z",
     "shell.execute_reply": "2024-05-07T15:13:57.787387Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "304ed28b-ab09-4a70-9c9b-e6985cb4dbc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:13:57.790772Z",
     "iopub.status.busy": "2024-05-07T15:13:57.790270Z",
     "iopub.status.idle": "2024-05-07T15:13:58.049707Z",
     "shell.execute_reply": "2024-05-07T15:13:58.049161Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]\n",
    "\n",
    "a = np.array([1, 0, 0, 1])\n",
    "N = len(my_protein)\n",
    "R = np.append(a, list(best_solution[0 : 2 * (N - 3)]))\n",
    "\n",
    "x = [0]\n",
    "y = [0]\n",
    "z = [0]\n",
    "for i in range(N - 1):\n",
    "    if (1 - R[2 * i]) * (1 - R[2 * i + 1]) == 1:\n",
    "        x.append(x[i] + (-1) ** (i + 1))\n",
    "        y.append(y[i] + (-1) ** (i))\n",
    "        z.append(z[i] + (-1) ** (i + 1))\n",
    "    if R[2 * i] * (1 - R[2 * i + 1]) == 1:\n",
    "        x.append(x[i] + (-1) ** (i + 1))\n",
    "        y.append(y[i] + (-1) ** (i + 1))\n",
    "        z.append(z[i] + (-1) ** (i))\n",
    "    if R[2 * i + 1] * (1 - R[2 * i]) == 1:\n",
    "        x.append(x[i] + (-1) ** (i))\n",
    "        y.append(y[i] + (-1) ** (i + 1))\n",
    "        z.append(z[i] + (-1) ** (i + 1))\n",
    "    if R[2 * i] * R[2 * i + 1] == 1:\n",
    "        x.append(x[i] + (-1) ** (i))\n",
    "        y.append(y[i] + (-1) ** (i))\n",
    "        z.append(z[i] + (-1) ** (i))\n",
    "\n",
    "fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z)])\n",
    "fig.update_scenes(xaxis_visible=False, yaxis_visible=False, zaxis_visible=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008bc051-0032-4852-97cd-8fb8ad158c31",
   "metadata": {},
   "source": [
    "## 5. Compare to Classical Results\n",
    "This section solves the optimization model for the defined amino squance by classical optimization, and presents the results, in order to compare to our QAOA performance. Mismatch of the classical and quantum solution might indicate the need to tune the QAOA parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2697bed1-ee01-465d-8b7c-547afc1ecd42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:13:58.071508Z",
     "iopub.status.busy": "2024-05-07T15:13:58.071009Z",
     "iopub.status.idle": "2024-05-07T15:13:58.173805Z",
     "shell.execute_reply": "2024-05-07T15:13:58.173153Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model protein_folding\n",
      "\n",
      "  Variables:\n",
      "    f : Size=6, Index=f_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          1 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          2 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          3 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          4 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          5 :     0 :   1.0 :     1 : False : False : Binary\n",
      "    interaction : Size=1, Index=interaction_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :   1.0 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True : -2600.0\n",
      "\n",
      "  Constraints:\n",
      "    None\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(protein_model)\n",
    "\n",
    "protein_model.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e254fc0d-83b2-4b4f-b0b7-e710a952cb11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:13:58.176311Z",
     "iopub.status.busy": "2024-05-07T15:13:58.175818Z",
     "iopub.status.idle": "2024-05-07T15:13:58.191579Z",
     "shell.execute_reply": "2024-05-07T15:13:58.191006Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_classical_solution = [pyo.value(protein_model.f[i]) for i in range(2 * (N - 3))]\n",
    "\n",
    "a = np.array([1, 0, 0, 1])\n",
    "N = len(my_protein)\n",
    "R_c = np.append(a, best_classical_solution)\n",
    "\n",
    "x = [0]\n",
    "y = [0]\n",
    "z = [0]\n",
    "for i in range(N - 1):\n",
    "    if (1 - R_c[2 * i]) * (1 - R_c[2 * i + 1]) == 1:\n",
    "        x.append(x[i] + (-1) ** (i + 1))\n",
    "        y.append(y[i] + (-1) ** (i))\n",
    "        z.append(z[i] + (-1) ** (i + 1))\n",
    "    if R_c[2 * i] * (1 - R_c[2 * i + 1]) == 1:\n",
    "        x.append(x[i] + (-1) ** (i + 1))\n",
    "        y.append(y[i] + (-1) ** (i + 1))\n",
    "        z.append(z[i] + (-1) ** (i))\n",
    "    if R_c[2 * i + 1] * (1 - R_c[2 * i]) == 1:\n",
    "        x.append(x[i] + (-1) ** (i))\n",
    "        y.append(y[i] + (-1) ** (i + 1))\n",
    "        z.append(z[i] + (-1) ** (i + 1))\n",
    "    if R_c[2 * i] * R_c[2 * i + 1] == 1:\n",
    "        x.append(x[i] + (-1) ** (i))\n",
    "        y.append(y[i] + (-1) ** (i))\n",
    "        z.append(z[i] + (-1) ** (i))\n",
    "\n",
    "fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z)])\n",
    "fig.update_scenes(xaxis_visible=False, yaxis_visible=False, zaxis_visible=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eebbb0b-33ee-4075-ab5b-9323ce413bb1",
   "metadata": {},
   "source": [
    "## References\n",
    "[1] Levinthal's paradox https://en.wikipedia.org/wiki/Levinthal%27s_paradox\n",
    "\n",
    "[2] Robert, Anton, Panagiotis Kl Barkoutsos, Stefan Woerner, and Ivano Tavernelli. \"Resource-efficient quantum algorithm for protein folding.\" npj Quantum Information 7, no. 1 (2021): 1-5.\n",
    "\n",
    "[3] Miyazawa, S. & Jernigan, R. L. Residue-residue potentials with a favorable contact\n",
    "pair term and an unfavorable high packing density term, for simulation and\n",
    "threading. J. Mol. Biol. 256, 623\u2013644 (1996).\n"
   ]
  }
 ],
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